
# Import libraries

import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px 

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)


# load data 
df = pd.read_csv('kaggle_survey_2020_responses.csv')
display(df.shape)

# remove the top row
df_fin = df.iloc[1:,:]

C:\Users\ugonn\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3165: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

(20037, 355)


# inspect the data and questions 

df.head()


# create a dictionary for questions 
Questions = {}

# create list of questions 
# not very efficient, but keeps things ordered
qnums = list(dict.fromkeys([i.split('_')[0] for i in df_fin.columns])) # returns a list of column names

# add data for each question to key value pairs in dictionary
for i in qnums:
    if i in ['Q1','Q2','Q3']: # since we are using .startswith() below this prevents all questions that start with 
        Questions[i] = df_fin[i] # [1,2,3] from going in the key value pair
    else:
        Questions[i] = df_fin[[q for q in df_fin.columns if q.startswith(i)]]


# create dictionary for different gender selections 
Genders = {}
for i in df_fin.Q2.unique():
    Genders[i] = df_fin[df_fin.Q2 == i]


# look at gender distribution
df_fin.Q2.value_counts() / df_fin.Q2.value_counts().sum()

Man                        0.788032
Woman                      0.193552
Prefer not to say          0.013126
Prefer to self-describe    0.002695
Nonbinary                  0.002595
Name: Q2, dtype: float64


# filter dataframe for male & female for simplicity (not that prefer not & nonbinary aren't important!)
df_mf = df_fin[df_fin.Q2.isin(['Man','Woman'])]


# DS is clearly already a male dominated field (or at least this sample of kaggle users is)
df_mf.Q2.value_counts() / df_mf.Q2.value_counts().sum()

Man      0.802817
Woman    0.197183
Name: Q2, dtype: float64


# Female Distribution by Role 
fig = px.histogram(df_mf, x='Q4', color='Q2')
fig.show()


# Female Distribution by Role Normalized by sample of respective population 
fig= px.histogram(df_mf, x='Q4',color='Q2', histnorm='probability density')
fig.show()


# Do more women have a particular Level of Education than men
male_degrees = df_mf[df_mf.Q2 == 'Man'].Q4.value_counts(normalize=True)
female_degrees = df_mf[df_mf.Q2 == 'Woman'].Q4.value_counts(normalize=True)
# total_degrees = df_mf.Q4.value_counts()
more_women = female_degrees - male_degrees # greater proportion of women than men
color = np.where(more_women.values < 0, 'red', 'blue')
fig = go.Figure(go.Bar(x=more_women.index, y=more_women.values, marker_color=color))
fig.update_layout(title= "How much more/less women are there in proportion to men for each Level of Education")
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()


# Percent more or less than distribution of the proportion of women
female_degrees = df_mf[df_mf.Q2 == 'Woman'].Q4.value_counts()
total_degrees = df_mf.Q4.value_counts()
more_women = (female_degrees / total_degrees) - .197 # greater proportion of women than sample proportion
color = np.where(more_women.values < 0, 'red', 'blue')
fig = go.Figure(go.Bar(x=more_women.index, y=more_women.values, marker_color=color))
fig.update_layout(title= "Level of Female Education Relative to Percentage of Sample women (19.7%)")
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()


# Gender Distribution by Country
fig = px.histogram(df_mf, x='Q3', color ='Q2')
fig.update_xaxes(categoryorder= "total descending")
fig.show()


# Percent more or less than distribution of the proportion of women 
female_country = df_mf[df_mf.Q2 == 'Woman'].Q3.value_counts()
total_country = df_mf.Q3.value_counts()
more_women = (female_country / total_country) - .197 # greater proportion of women than sample
color = np.where(more_women.values < 0, 'red', 'blue')
fig = go.Figure(go.Bar(x=more_women.index, y=more_women.values, marker_color=color))
fig.update_layout(title= "Amount of Women By Country Relative to Percentage of Sample women (19.7%)")
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()


# function for creating new plots without having to repeat all that code 
def create_norm_graph(qnum, data, title, baseline):
    '''
    This function creates interactive plots using plotly graph objects.
    The function makes a bar plot that shows how much a category performed relative to a baseline provided.
    
    Args:
        qnum: column from the dataframe
        data: the dataframe
        title: title for the plot
        baseline: baseline to compare values of the column with
        
    Returns:
        The bar plot showing proportions relative to a baseline
    '''
    
    male = data[data.Q2 == 'Man'][qnum].value_counts()
    female = data[data.Q2 == 'Woman'][qnum].value_counts()
    total = data[qnum].value_counts()
    more_women = (female / total) - baseline # greater proportion of women than sample
    color = np.where(more_women.values < 0, 'red', 'blue')
    fig = go.Figure(go.Bar(x=more_women.index, y=more_women.values, marker_color=color))
    fig.update_layout(title= title)
    fig.update_layout(xaxis={'categoryorder':'total descending'})
    fig.show()
    return


# which countries have the most relative female representitives in the survey?
create_norm_graph('Q3', df_mf, "Amount of Women By Country Relative to Percentage of Sample women (19.7%)", .197)


# Which roles have the most women relative to the baseline?
create_norm_graph('Q5', df_mf, "Amount of Women By Role Relative to Percentage of Sample women (19.7%)", .197)


# create new baseline for only employed people since we want to look at earning potential
df_workers_mf = df_mf[~df_mf['Q5'].isin(['Student', 'Currently not employed'])]
df_workers_mf.Q2.value_counts() / df_workers_mf.Q2.value_counts().sum()

Man      0.825956
Woman    0.174044
Name: Q2, dtype: float64


# Women's experience 
create_norm_graph('Q6', df_workers_mf, "Amount of Women By Experience Relative to Percentage of Sample women (17.4%)", .174)


# by income level 
create_norm_graph('Q24', df_workers_mf, "Amount of Women By Income Level Relative to Percentage of Sample women (17.4%)", .174)


# graph for just data scientists 
df_mf_ds = df_mf[df_mf['Q5'] =='Data Scientist']
create_norm_graph('Q24', df_mf_ds, 
                  "Amount of Female Data Scientists By Income Level Relative to Percentage of Sample women (17.4%)", .174)


# count for perspective, some sample size issues here
df_mf_ds.Q24.value_counts()

$0-999             439
100,000-124,999    163
10,000-14,999      136
40,000-49,999      130
30,000-39,999      119
150,000-199,999    108
125,000-149,999    106
50,000-59,999      103
1,000-1,999        103
70,000-79,999       99
15,000-19,999       95
60,000-69,999       87
20,000-24,999       86
90,000-99,999       84
25,000-29,999       84
5,000-7,499         75
80,000-89,999       75
7,500-9,999         64
2,000-2,999         45
3,000-3,999         44
4,000-4,999         43
200,000-249,999     38
300,000-500,000     16
> $500,000          12
250,000-299,999      8
Name: Q24, dtype: int64


# graph for US 
df_mf_US = df_mf[df_mf['Q3'] == 'United States of America']
create_norm_graph('Q24', df_mf_US, "Amount of Women in the US Relative to Percentage of Sample women (17.4%)", .174)


df_mf_US.Q24.value_counts()

100,000-124,999    244
150,000-199,999    225
125,000-149,999    179
90,000-99,999      110
$0-999              99
70,000-79,999       90
200,000-249,999     84
80,000-89,999       82
60,000-69,999       58
50,000-59,999       47
40,000-49,999       43
300,000-500,000     36
250,000-299,999     30
30,000-39,999       25
10,000-14,999       15
25,000-29,999       12
15,000-19,999       12
> $500,000          11
20,000-24,999       11
1,000-1,999          9
5,000-7,499          6
2,000-2,999          4
7,500-9,999          3
3,000-3,999          3
4,000-4,999          2
Name: Q24, dtype: int64


# Income by roles. Filtering for the top salary ranges.
condition = df_fin['Q24'].isin(['200,000-249,999', '250,000-299,999', '300,000-500,000', '> $500,000'])

fig = px.histogram(df_fin.dropna(subset=['Q24','Q5'])[condition], x='Q24', color ='Q5')
fig.update_xaxes(categoryorder="total descending")
fig.update_layout(title='Distribution of Roles among the top salary ranges')
fig.show()

<ipython-input-25-73f30e96e730>:4: UserWarning:

Boolean Series key will be reindexed to match DataFrame index.


# Income by years of coding experience. Filtering for the top salary ranges.
condition = df_fin['Q24'].isin(['200,000-249,999', '250,000-299,999', '300,000-500,000', '> $500,000'])

fig = px.histogram(df_fin.dropna(subset=['Q24','Q6'])[condition], x='Q24', color='Q6')
fig.update_xaxes(categoryorder="total descending")
fig.update_layout(title='Distribution of Years of coding experience among the top salary ranges')
fig.show()

<ipython-input-26-4db175ae7a1d>:4: UserWarning:

Boolean Series key will be reindexed to match DataFrame index.


# Income by level of education. Filtering for the top salary ranges.
condition = df_fin['Q24'].isin(['200,000-249,999', '250,000-299,999', '300,000-500,000', '> $500,000'])

fig = px.histogram(df_fin.dropna(subset=['Q24','Q4'])[condition], x='Q24', color ='Q4')
fig.update_xaxes(categoryorder="total descending")
fig.update_layout(title='Distribution of Levels of Education among the top salary ranges')
fig.show()

<ipython-input-27-eddb7ae91618>:4: UserWarning:

Boolean Series key will be reindexed to match DataFrame index.


# replace '$',',','>' in data 
df_model = df_fin.dropna(subset=['Q24'])
df_model['salary_cleaned'] = df_model.Q24.apply(lambda x: str(x).replace('$', '').replace(',', '').replace('>', '').strip())
df_model.salary_cleaned.value_counts()

<ipython-input-28-a6a7a913391f>:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

0-999            2128
10000-14999       665
1000-1999         581
100000-124999     573
40000-49999       552
30000-39999       540
50000-59999       510
5000-7499         488
15000-19999       449
60000-69999       408
20000-24999       404
70000-79999       394
7500-9999         371
150000-199999     347
2000-2999         330
125000-149999     315
25000-29999       310
90000-99999       280
4000-4999         279
80000-89999       273
3000-3999         264
200000-249999     115
300000-500000      55
500000             50
250000-299999      48
Name: salary_cleaned, dtype: int64


# create min range and max range for salary 
df_model['salary_min'] = df_model.salary_cleaned.apply(lambda x: 500000 if '-' not in x else int(x.split('-')[0]))
df_model['salary_max'] = df_model.salary_cleaned.apply(lambda x: 500000 if '-' not in x else int(x.split('-')[1]))

df_model.salary_max.value_counts()

<ipython-input-29-3f085ad11601>:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-29-3f085ad11601>:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

999       2128
14999      665
1999       581
124999     573
49999      552
39999      540
59999      510
7499       488
19999      449
69999      408
24999      404
79999      394
9999       371
199999     347
2999       330
149999     315
29999      310
99999      280
4999       279
89999      273
3999       264
249999     115
500000     105
299999      48
Name: salary_max, dtype: int64


# Convert to rough continuous variable 
df_model['aprox_salary'] = (df_model.salary_min + df_model.salary_max) / 2
df_model.aprox_salary.value_counts()

<ipython-input-30-78a955329e39>:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

499.5       2128
12499.5      665
1499.5       581
112499.5     573
44999.5      552
34999.5      540
54999.5      510
6249.5       488
17499.5      449
64999.5      408
22499.5      404
74999.5      394
8749.5       371
174999.5     347
2499.5       330
137499.5     315
27499.5      310
94999.5      280
4499.5       279
84999.5      273
3499.5       264
224999.5     115
400000.0      55
500000.0      50
274999.5      48
Name: aprox_salary, dtype: int64


# simple linear regression just gender 
import statsmodels.api as sm


# filter for men & women 
df_model_fin = df_model[df_model.Q2.isin(['Man', 'Woman'])]

# filter for workers 
df_model_fin = df_model_fin[~df_model_fin['Q5'].isin(['Student', 'Currently not employed'])]
df_model_fin.drop('Time from Start to Finish (seconds)', axis=1, inplace=True)


# check for null values in columns
df_model_fin.isnull().any()

Q1                False
Q2                False
Q3                False
Q4                False
Q5                False
Q6                False
Q7_Part_1          True
Q7_Part_2          True
Q7_Part_3          True
Q7_Part_4          True
Q7_Part_5          True
Q7_Part_6          True
Q7_Part_7          True
Q7_Part_8          True
Q7_Part_9          True
Q7_Part_10         True
Q7_Part_11         True
Q7_Part_12         True
Q7_OTHER           True
Q8                 True
Q9_Part_1          True
Q9_Part_2          True
Q9_Part_3          True
Q9_Part_4          True
Q9_Part_5          True
Q9_Part_6          True
Q9_Part_7          True
Q9_Part_8          True
Q9_Part_9          True
Q9_Part_10         True
Q9_Part_11         True
Q9_OTHER           True
Q10_Part_1         True
Q10_Part_2         True
Q10_Part_3         True
Q10_Part_4         True
Q10_Part_5         True
Q10_Part_6         True
Q10_Part_7         True
Q10_Part_8         True
Q10_Part_9         True
Q10_Part_10        True
Q10_Part_11        True
Q10_Part_12        True
Q10_Part_13        True
Q10_OTHER          True
Q11                True
Q12_Part_1         True
Q12_Part_2         True
Q12_Part_3         True
Q12_OTHER          True
Q13                True
Q14_Part_1         True
Q14_Part_2         True
Q14_Part_3         True
Q14_Part_4         True
Q14_Part_5         True
Q14_Part_6         True
Q14_Part_7         True
Q14_Part_8         True
Q14_Part_9         True
Q14_Part_10        True
Q14_Part_11        True
Q14_OTHER          True
Q15                True
Q16_Part_1         True
Q16_Part_2         True
Q16_Part_3         True
Q16_Part_4         True
Q16_Part_5         True
Q16_Part_6         True
Q16_Part_7         True
Q16_Part_8         True
Q16_Part_9         True
Q16_Part_10        True
Q16_Part_11        True
Q16_Part_12        True
Q16_Part_13        True
Q16_Part_14        True
Q16_Part_15        True
Q16_OTHER          True
Q17_Part_1         True
Q17_Part_2         True
Q17_Part_3         True
Q17_Part_4         True
Q17_Part_5         True
Q17_Part_6         True
Q17_Part_7         True
Q17_Part_8         True
Q17_Part_9         True
Q17_Part_10        True
Q17_Part_11        True
Q17_OTHER          True
Q18_Part_1         True
Q18_Part_2         True
Q18_Part_3         True
Q18_Part_4         True
Q18_Part_5         True
Q18_Part_6         True
Q18_OTHER          True
Q19_Part_1         True
Q19_Part_2         True
Q19_Part_3         True
Q19_Part_4         True
Q19_Part_5         True
Q19_OTHER          True
Q20               False
Q21               False
Q22               False
Q23_Part_1         True
Q23_Part_2         True
Q23_Part_3         True
Q23_Part_4         True
Q23_Part_5         True
Q23_Part_6         True
Q23_Part_7         True
Q23_OTHER          True
Q24               False
Q25                True
Q26_A_Part_1       True
Q26_A_Part_2       True
Q26_A_Part_3       True
Q26_A_Part_4       True
Q26_A_Part_5       True
Q26_A_Part_6       True
Q26_A_Part_7       True
Q26_A_Part_8       True
Q26_A_Part_9       True
Q26_A_Part_10      True
Q26_A_Part_11      True
Q26_A_OTHER        True
Q27_A_Part_1       True
Q27_A_Part_2       True
Q27_A_Part_3       True
Q27_A_Part_4       True
Q27_A_Part_5       True
Q27_A_Part_6       True
Q27_A_Part_7       True
Q27_A_Part_8       True
Q27_A_Part_9       True
Q27_A_Part_10      True
Q27_A_Part_11      True
Q27_A_OTHER        True
Q28_A_Part_1       True
Q28_A_Part_2       True
Q28_A_Part_3       True
Q28_A_Part_4       True
Q28_A_Part_5       True
Q28_A_Part_6       True
Q28_A_Part_7       True
Q28_A_Part_8       True
Q28_A_Part_9       True
Q28_A_Part_10      True
Q28_A_OTHER        True
Q29_A_Part_1       True
Q29_A_Part_2       True
Q29_A_Part_3       True
Q29_A_Part_4       True
Q29_A_Part_5       True
Q29_A_Part_6       True
Q29_A_Part_7       True
Q29_A_Part_8       True
Q29_A_Part_9       True
Q29_A_Part_10      True
Q29_A_Part_11      True
Q29_A_Part_12      True
Q29_A_Part_13      True
Q29_A_Part_14      True
Q29_A_Part_15      True
Q29_A_Part_16      True
Q29_A_Part_17      True
Q29_A_OTHER        True
Q30                True
Q31_A_Part_1       True
Q31_A_Part_2       True
Q31_A_Part_3       True
Q31_A_Part_4       True
Q31_A_Part_5       True
Q31_A_Part_6       True
Q31_A_Part_7       True
Q31_A_Part_8       True
Q31_A_Part_9       True
Q31_A_Part_10      True
Q31_A_Part_11      True
Q31_A_Part_12      True
Q31_A_Part_13      True
Q31_A_Part_14      True
Q31_A_OTHER        True
Q32                True
Q33_A_Part_1       True
Q33_A_Part_2       True
Q33_A_Part_3       True
Q33_A_Part_4       True
Q33_A_Part_5       True
Q33_A_Part_6       True
Q33_A_Part_7       True
Q33_A_OTHER        True
Q34_A_Part_1       True
Q34_A_Part_2       True
Q34_A_Part_3       True
Q34_A_Part_4       True
Q34_A_Part_5       True
Q34_A_Part_6       True
Q34_A_Part_7       True
Q34_A_Part_8       True
Q34_A_Part_9       True
Q34_A_Part_10      True
Q34_A_Part_11      True
Q34_A_OTHER        True
Q35_A_Part_1       True
Q35_A_Part_2       True
Q35_A_Part_3       True
Q35_A_Part_4       True
Q35_A_Part_5       True
Q35_A_Part_6       True
Q35_A_Part_7       True
Q35_A_Part_8       True
Q35_A_Part_9       True
Q35_A_Part_10      True
Q35_A_OTHER        True
Q36_Part_1         True
Q36_Part_2         True
Q36_Part_3         True
Q36_Part_4         True
Q36_Part_5         True
Q36_Part_6         True
Q36_Part_7         True
Q36_Part_8         True
Q36_Part_9         True
Q36_OTHER          True
Q37_Part_1         True
Q37_Part_2         True
Q37_Part_3         True
Q37_Part_4         True
Q37_Part_5         True
Q37_Part_6         True
Q37_Part_7         True
Q37_Part_8         True
Q37_Part_9         True
Q37_Part_10        True
Q37_Part_11        True
Q37_OTHER          True
Q38                True
Q39_Part_1         True
Q39_Part_2         True
Q39_Part_3         True
Q39_Part_4         True
Q39_Part_5         True
Q39_Part_6         True
Q39_Part_7         True
Q39_Part_8         True
Q39_Part_9         True
Q39_Part_10        True
Q39_Part_11        True
Q39_OTHER          True
Q26_B_Part_1       True
Q26_B_Part_2       True
Q26_B_Part_3       True
Q26_B_Part_4       True
Q26_B_Part_5       True
Q26_B_Part_6       True
Q26_B_Part_7       True
Q26_B_Part_8       True
Q26_B_Part_9       True
Q26_B_Part_10      True
Q26_B_Part_11      True
Q26_B_OTHER        True
Q27_B_Part_1       True
Q27_B_Part_2       True
Q27_B_Part_3       True
Q27_B_Part_4       True
Q27_B_Part_5       True
Q27_B_Part_6       True
Q27_B_Part_7       True
Q27_B_Part_8       True
Q27_B_Part_9       True
Q27_B_Part_10      True
Q27_B_Part_11      True
Q27_B_OTHER        True
Q28_B_Part_1       True
Q28_B_Part_2       True
Q28_B_Part_3       True
Q28_B_Part_4       True
Q28_B_Part_5       True
Q28_B_Part_6       True
Q28_B_Part_7       True
Q28_B_Part_8       True
Q28_B_Part_9       True
Q28_B_Part_10      True
Q28_B_OTHER        True
Q29_B_Part_1       True
Q29_B_Part_2       True
Q29_B_Part_3       True
Q29_B_Part_4       True
Q29_B_Part_5       True
Q29_B_Part_6       True
Q29_B_Part_7       True
Q29_B_Part_8       True
Q29_B_Part_9       True
Q29_B_Part_10      True
Q29_B_Part_11      True
Q29_B_Part_12      True
Q29_B_Part_13      True
Q29_B_Part_14      True
Q29_B_Part_15      True
Q29_B_Part_16      True
Q29_B_Part_17      True
Q29_B_OTHER        True
Q31_B_Part_1       True
Q31_B_Part_2       True
Q31_B_Part_3       True
Q31_B_Part_4       True
Q31_B_Part_5       True
Q31_B_Part_6       True
Q31_B_Part_7       True
Q31_B_Part_8       True
Q31_B_Part_9       True
Q31_B_Part_10      True
Q31_B_Part_11      True
Q31_B_Part_12      True
Q31_B_Part_13      True
Q31_B_Part_14      True
Q31_B_OTHER        True
Q33_B_Part_1       True
Q33_B_Part_2       True
Q33_B_Part_3       True
Q33_B_Part_4       True
Q33_B_Part_5       True
Q33_B_Part_6       True
Q33_B_Part_7       True
Q33_B_OTHER        True
Q34_B_Part_1       True
Q34_B_Part_2       True
Q34_B_Part_3       True
Q34_B_Part_4       True
Q34_B_Part_5       True
Q34_B_Part_6       True
Q34_B_Part_7       True
Q34_B_Part_8       True
Q34_B_Part_9       True
Q34_B_Part_10      True
Q34_B_Part_11      True
Q34_B_OTHER        True
Q35_B_Part_1       True
Q35_B_Part_2       True
Q35_B_Part_3       True
Q35_B_Part_4       True
Q35_B_Part_5       True
Q35_B_Part_6       True
Q35_B_Part_7       True
Q35_B_Part_8       True
Q35_B_Part_9       True
Q35_B_Part_10      True
Q35_B_OTHER        True
salary_cleaned    False
salary_min        False
salary_max        False
aprox_salary      False
dtype: bool


# create dummy variables, this is needed because essentially all our data is categorical
model_dummies = pd.get_dummies(df_model_fin)
model_dummies.head()


# We only need one gender in this case because we trimmed it to only have Men & Women
Y = model_dummies.aprox_salary
X = model_dummies.Q2_Woman


# for statsmodels, we need to add a constant to create intercept 
X = sm.add_constant(X)


# fit model with data 
model = sm.OLS(Y, X)
results = model.fit()


# create summary report (watch video to see interpretation)
results.summary()


# create function to add additional questions to dataframe for easier processing
def qnums(question_list, dataframe):
    '''
    This function selects the dummy columns from the dummy dataframe for the related questions 
    passed into the question_list argument.
    
    Args:
        question_list: columns that you want their dummy representatives.
        dataframe: the dataframe that contains dummy variables for all columns.
        
    Returns:
        A dataframe containing dummy representatives for selected questions.
    '''
    q_out = [] 
    for i in question_list:
        for j in dataframe.columns:
            if i == j.split('_')[0]:
                q_out.append(j)
    return dataframe.loc[:, q_out]
        
# create data for questions 2, 4 and 5
q245 =  qnums(['Q2','Q4','Q5'], model_dummies)
q245.head()


# drop one of each of the columns, they are redundant
X = q245.drop(['Q2_Man', 'Q4_I prefer not to answer', 'Q5_Other'], axis=1)
X = sm.add_constant(X)


# build model with additional features education, gender, and role 
model = sm.OLS(Y, X)
results = model.fit()
results.summary()


# questions 2, 4, 5 and 7 (add in programming languages)
q2457 =  qnums(['Q2', 'Q4', 'Q5', 'Q7'], model_dummies)
q2457.head()


X = q2457.drop(['Q2_Man', 'Q4_I prefer not to answer', 'Q5_Other', 'Q7_OTHER_Other'], axis=1)
X = sm.add_constant(X)


model = sm.OLS(Y, X)
results = model.fit()
results.summary()


# questions 2, 3, 4, 5 and 7, added country (huge boost in model performance)
        
q24573 =  qnums(['Q2','Q4','Q5','Q7','Q3'], model_dummies)
q24573.head()


X = q24573.drop(['Q2_Man', 'Q4_I prefer not to answer', 'Q5_Other', 'Q7_OTHER_Other', 'Q3_Other'], axis=1)
X = sm.add_constant(X)


model = sm.OLS(Y,X)
results= model.fit()
results.summary()


# questions 2, 3, 4, 5, 6, 7 and 20
        
q245736 =  qnums(['Q2','Q4','Q5','Q7','Q3','Q6','Q20'], model_dummies)

X = q245736.drop(['Q2_Man', 'Q4_I prefer not to answer', 'Q5_Other', 
                  'Q7_OTHER_Other', 'Q3_Other', 'Q6_I have never written code', 'Q20_50-249 employees'], axis=1)
X = sm.add_constant(X)


model2 = sm.OLS(Y, X)
results = model2.fit()
results.summary()


# fit model with lasso parameters, set alpha high enough to eliminate some variables 
results_reg = model2.fit_regularized(L1_wt=1, alpha=5)
# L1_wt set to 1 means Lasso Regression, 0 means ridge regression. setting alpha above zero removes variables that are not as
# important. Higher alpha means more variables that are cut off. an alpha of zero is basically a Linear Regression.

final = sm.regression.linear_model.OLSResults(model2, results_reg.params, model2.normalized_cov_params)
print(final.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:           aprox_salary   R-squared:                       0.391
Model:                            OLS   Adj. R-squared:                  0.385
Method:                 Least Squares   F-statistic:                     72.13
Date:                Wed, 27 Jul 2022   Prob (F-statistic):               0.00
Time:                        15:55:03   Log-Likelihood:            -1.2931e+05
No. Observations:               10555   AIC:                         2.588e+05
Df Residuals:                   10461   BIC:                         2.595e+05
Df Model:                          93                                         
Covariance Type:            nonrobust                                         
========================================================================================================================================
                                                                           coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------------------------------------------------------------
const                                                                 2.728e+04   5231.137      5.214      0.000     1.7e+04    3.75e+04
Q2_Woman                                                             -9926.8810   1392.319     -7.130      0.000   -1.27e+04   -7197.671
Q4_Bachelor’s degree                                                          0   4365.116          0      1.000   -8556.460    8556.460
Q4_Doctoral degree                                                    8611.7882   4516.913      1.907      0.057    -242.223    1.75e+04
Q4_Master’s degree                                                    3941.9186   4333.081      0.910      0.363   -4551.746    1.24e+04
Q4_No formal education past high school                              -5660.8662   6556.924     -0.863      0.388   -1.85e+04    7191.957
Q4_Professional degree                                                7058.9360   4885.620      1.445      0.149   -2517.812    1.66e+04
Q4_Some college/university study without earning a bachelor’s degree -1644.8231   5006.714     -0.329      0.743   -1.15e+04    8169.292
Q5_Business Analyst                                                           0   2400.692          0      1.000   -4705.815    4705.815
Q5_DBA/Database Engineer                                             -6391.6307   5181.507     -1.234      0.217   -1.65e+04    3765.112
Q5_Data Analyst                                                      -7802.4291   2009.695     -3.882      0.000   -1.17e+04   -3863.043
Q5_Data Engineer                                                     -2694.7072   3040.976     -0.886      0.376   -8655.601    3266.187
Q5_Data Scientist                                                     5237.0525   1770.594      2.958      0.003    1766.349    8707.756
Q5_Machine Learning Engineer                                          2832.5290   2235.111      1.267      0.205   -1548.715    7213.773
Q5_Product/Project Manager                                            1.183e+04   2515.800      4.701      0.000    6895.020    1.68e+04
Q5_Research Scientist                                                -6960.5172   2248.454     -3.096      0.002   -1.14e+04   -2553.118
Q5_Software Engineer                                                 -4540.9729   1996.314     -2.275      0.023   -8454.129    -627.817
Q5_Statistician                                                      -4928.0490   3615.055     -1.363      0.173    -1.2e+04    2158.148
Q7_Part_1_Python                                                      1979.7206   1575.938      1.256      0.209   -1109.419    5068.861
Q7_Part_2_R                                                                   0   1246.878          0      1.000   -2444.120    2444.120
Q7_Part_3_SQL                                                         1323.3089   1126.446      1.175      0.240    -884.741    3531.359
Q7_Part_4_C                                                            218.4105   1705.003      0.128      0.898   -3123.721    3560.542
Q7_Part_5_C++                                                        -1988.5873   1570.186     -1.266      0.205   -5066.451    1089.276
Q7_Part_6_Java                                                         170.9445   1499.143      0.114      0.909   -2767.661    3109.550
Q7_Part_7_Javascript                                                 -3373.5353   1480.807     -2.278      0.023   -6276.200    -470.871
Q7_Part_8_Julia                                                       8813.2997   3997.872      2.204      0.028     976.708    1.66e+04
Q7_Part_9_Swift                                                       5223.8990   4631.314      1.128      0.259   -3854.359    1.43e+04
Q7_Part_10_Bash                                                       7706.9081   1584.276      4.865      0.000    4601.425    1.08e+04
Q7_Part_11_MATLAB                                                    -5103.2229   1730.979     -2.948      0.003   -8496.271   -1710.174
Q7_Part_12_None                                                      -7145.0805   5351.876     -1.335      0.182   -1.76e+04    3345.617
Q3_Argentina                                                         -2.049e+04   5952.685     -3.442      0.001   -3.22e+04   -8822.552
Q3_Australia                                                          4.677e+04   4695.513      9.960      0.000    3.76e+04     5.6e+04
Q3_Bangladesh                                                        -1.848e+04   7734.993     -2.389      0.017   -3.36e+04   -3318.267
Q3_Belarus                                                           -1.693e+04   8571.038     -1.976      0.048   -3.37e+04    -133.945
Q3_Belgium                                                              1.3e+04   8802.819      1.477      0.140   -4254.470    3.03e+04
Q3_Brazil                                                            -1.579e+04   3038.222     -5.199      0.000   -2.18e+04   -9839.196
Q3_Canada                                                             3.381e+04   4073.465      8.301      0.000    2.58e+04    4.18e+04
Q3_Chile                                                             -1.013e+04   6728.724     -1.506      0.132   -2.33e+04    3057.320
Q3_China                                                                      0   4415.367          0      1.000   -8654.962    8654.962
Q3_Colombia                                                          -2.085e+04   4999.481     -4.170      0.000   -3.06e+04    -1.1e+04
Q3_Egypt                                                             -1.605e+04   5647.730     -2.842      0.004   -2.71e+04   -4982.774
Q3_France                                                                     0   4110.578          0      1.000   -8057.517    8057.517
Q3_Germany                                                            2.943e+04   3721.836      7.908      0.000    2.21e+04    3.67e+04
Q3_Ghana                                                                      0   1.13e+04          0      1.000   -2.21e+04    2.21e+04
Q3_Greece                                                            -1.008e+04   6573.158     -1.534      0.125    -2.3e+04    2800.364
Q3_India                                                             -1.364e+04   2158.235     -6.320      0.000   -1.79e+04   -9408.762
Q3_Indonesia                                                         -6613.1198   4980.712     -1.328      0.184   -1.64e+04    3150.026
Q3_Iran, Islamic Republic of...                                      -2.322e+04   6106.703     -3.803      0.000   -3.52e+04   -1.13e+04
Q3_Ireland                                                            1.629e+04   8926.541      1.824      0.068   -1211.571    3.38e+04
Q3_Israel                                                             6.778e+04   6723.221     10.082      0.000    5.46e+04     8.1e+04
Q3_Italy                                                             -3291.3737   4199.447     -0.784      0.433   -1.15e+04    4940.343
Q3_Japan                                                              4627.1024   3220.332      1.437      0.151   -1685.362    1.09e+04
Q3_Kenya                                                             -1.462e+04   6307.687     -2.318      0.020    -2.7e+04   -2259.632
Q3_Malaysia                                                          -8250.6329   7171.333     -1.151      0.250   -2.23e+04    5806.548
Q3_Mexico                                                            -1.304e+04   4727.024     -2.760      0.006   -2.23e+04   -3778.765
Q3_Morocco                                                           -1.599e+04   6870.934     -2.328      0.020   -2.95e+04   -2523.777
Q3_Nepal                                                             -6973.7885   1.15e+04     -0.605      0.545   -2.96e+04    1.56e+04
Q3_Netherlands                                                        2.665e+04   5239.321      5.087      0.000    1.64e+04    3.69e+04
Q3_Nigeria                                                             -1.4e+04   3772.132     -3.711      0.000   -2.14e+04   -6605.740
Q3_Pakistan                                                            -1.3e+04   4996.248     -2.602      0.009   -2.28e+04   -3204.645
Q3_Peru                                                              -1.491e+04   6722.817     -2.218      0.027   -2.81e+04   -1731.599
Q3_Philippines                                                        -1.29e+04   7510.388     -1.718      0.086   -2.76e+04    1818.448
Q3_Poland                                                            -8300.2996   5512.930     -1.506      0.132   -1.91e+04    2506.095
Q3_Portugal                                                          -7485.7523   5799.759     -1.291      0.197   -1.89e+04    3882.883
Q3_Republic of Korea                                                          0   8076.642          0      1.000   -1.58e+04    1.58e+04
Q3_Romania                                                           -1.346e+04   8467.008     -1.589      0.112   -3.01e+04    3141.280
Q3_Russia                                                            -1.483e+04   3303.962     -4.487      0.000   -2.13e+04   -8349.045
Q3_Saudi Arabia                                                               0   7653.867          0      1.000    -1.5e+04     1.5e+04
Q3_Singapore                                                          1.855e+04   5769.149      3.215      0.001    7236.425    2.99e+04
Q3_South Africa                                                               0   6013.578          0      1.000   -1.18e+04    1.18e+04
Q3_South Korea                                                                0   5766.424          0      1.000   -1.13e+04    1.13e+04
Q3_Spain                                                                      0   3812.461          0      1.000   -7473.152    7473.152
Q3_Sri Lanka                                                         -1.325e+04   8470.344     -1.564      0.118   -2.99e+04    3351.680
Q3_Sweden                                                              1.31e+04   7170.317      1.827      0.068    -952.709    2.72e+04
Q3_Switzerland                                                        7.549e+04   7586.516      9.950      0.000    6.06e+04    9.04e+04
Q3_Taiwan                                                             -1.04e+04   4975.790     -2.091      0.037   -2.02e+04    -650.887
Q3_Thailand                                                          -2104.3487   6287.450     -0.335      0.738   -1.44e+04    1.02e+04
Q3_Tunisia                                                           -5128.2497   7459.117     -0.688      0.492   -1.97e+04    9493.043
Q3_Turkey                                                            -1.626e+04   4399.990     -3.696      0.000   -2.49e+04   -7636.442
Q3_Ukraine                                                           -1.277e+04   5146.601     -2.481      0.013   -2.29e+04   -2679.326
Q3_United Arab Emirates                                               2.749e+04   7806.692      3.522      0.000    1.22e+04    4.28e+04
Q3_United Kingdom of Great Britain and Northern Ireland               3.812e+04   3320.197     11.482      0.000    3.16e+04    4.46e+04
Q3_United States of America                                           7.577e+04   2297.890     32.974      0.000    7.13e+04    8.03e+04
Q3_Viet Nam                                                          -1.548e+04   5895.553     -2.625      0.009    -2.7e+04   -3920.557
Q6_1-2 years                                                         -4748.7300   2923.570     -1.624      0.104   -1.05e+04     982.024
Q6_10-20 years                                                        2.119e+04   3069.428      6.904      0.000    1.52e+04    2.72e+04
Q6_20+ years                                                          2.923e+04   3188.047      9.168      0.000     2.3e+04    3.55e+04
Q6_3-5 years                                                         -2181.5607   2902.063     -0.752      0.452   -7870.158    3507.037
Q6_5-10 years                                                         8524.6558   2976.632      2.864      0.004    2689.890    1.44e+04
Q6_< 1 years                                                         -4605.4944   3002.044     -1.534      0.125   -1.05e+04    1279.086
Q20_0-49 employees                                                   -8368.2556   1547.299     -5.408      0.000   -1.14e+04   -5335.255
Q20_10,000 or more employees                                          1.074e+04   1741.227      6.170      0.000    7330.148    1.42e+04
Q20_1000-9,999 employees                                              4940.7687   1767.952      2.795      0.005    1475.245    8406.292
Q20_250-999 employees                                                 2276.2734   1931.768      1.178      0.239   -1510.360    6062.906
==============================================================================
Omnibus:                     9307.711   Durbin-Watson:                   1.978
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           436263.322
Skew:                           4.085   Prob(JB):                         0.00
Kurtosis:                      33.418   Cond. No.                         52.9
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.


from sklearn.ensemble import RandomForestRegressor


# compare random forest feature importance (allows us to rank)
clf_rf = RandomForestRegressor()
clf_rf.fit(X, Y)

RandomForestRegressor()


feat_importances = pd.Series(clf_rf.feature_importances_, index=X.columns)
ax = feat_importances.nlargest(25).sort_values().plot(kind='barh', figsize=(6, 12))
ax.barh([7], feat_importances.loc['Q2_Woman'], color='red')

<BarContainer object of 1 artists>


# build models for men and women independently. See how they estimate salary on the same data 
# I think this is also a decent way to isolate individual effects of education, country, etc.
Women_Model = model_dummies[model_dummies.Q2_Woman == 1]
Men_Model = model_dummies[model_dummies.Q2_Woman == 0]


# create and train women's model 
women_fin = qnums(['Q4', 'Q5', 'Q7', 'Q3', 'Q6', 'Q20'], Women_Model)
Y_W = Women_Model.aprox_salary
X_W = women_fin.drop(['Q4_I prefer not to answer', 'Q5_Other', 
                  'Q7_OTHER_Other', 'Q3_Other', 'Q6_I have never written code', 'Q20_50-249 employees'], axis=1)
X_W = sm.add_constant(X_W)

Women_Model.head()


model_W = sm.OLS(Y_W, X_W)
results_W = model_W.fit()
results_W.summary()


results_reg_W = model_W.fit_regularized(L1_wt=1, alpha=5)
final_W = sm.regression.linear_model.OLSResults(model_W, results_reg_W.params, model_W.normalized_cov_params)
print(final_W.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:           aprox_salary   R-squared:                       0.453
Model:                            OLS   Adj. R-squared:                  0.421
Method:                 Least Squares   F-statistic:                     14.30
Date:                Wed, 27 Jul 2022   Prob (F-statistic):          2.41e-149
Time:                        15:55:30   Log-Likelihood:                -20268.
No. Observations:                1683   AIC:                         4.072e+04
Df Residuals:                    1590   BIC:                         4.123e+04
Df Model:                          92                                         
Covariance Type:            nonrobust                                         
========================================================================================================================================
                                                                           coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------------------------------------------------------------
const                                                                 1.789e+04   1.07e+04      1.667      0.096   -3164.903    3.89e+04
Q4_Bachelor’s degree                                                  -753.1711   9331.157     -0.081      0.936   -1.91e+04    1.75e+04
Q4_Doctoral degree                                                    9596.4453   9542.889      1.006      0.315   -9121.522    2.83e+04
Q4_Master’s degree                                                    5866.7117   9266.697      0.633      0.527   -1.23e+04     2.4e+04
Q4_No formal education past high school                                       0   2.37e+04          0      1.000   -4.65e+04    4.65e+04
Q4_Professional degree                                                6823.2280   1.04e+04      0.653      0.514   -1.37e+04    2.73e+04
Q4_Some college/university study without earning a bachelor’s degree -7174.8753   1.17e+04     -0.616      0.538      -3e+04    1.57e+04
Q5_Business Analyst                                                   6725.2876   4840.848      1.389      0.165   -2769.828    1.62e+04
Q5_DBA/Database Engineer                                             -9487.4445    1.3e+04     -0.730      0.465    -3.5e+04     1.6e+04
Q5_Data Analyst                                                      -1691.5593   3926.075     -0.431      0.667   -9392.387    6009.269
Q5_Data Engineer                                                      7962.8922   6844.670      1.163      0.245   -5462.635    2.14e+04
Q5_Data Scientist                                                     8706.3022   3766.402      2.312      0.021    1318.667    1.61e+04
Q5_Machine Learning Engineer                                          -108.8149   5252.564     -0.021      0.983   -1.04e+04    1.02e+04
Q5_Product/Project Manager                                            1.914e+04   6230.018      3.073      0.002    6923.522    3.14e+04
Q5_Research Scientist                                                -3598.9057   4545.622     -0.792      0.429   -1.25e+04    5317.137
Q5_Software Engineer                                                          0   4459.152          0      1.000   -8746.436    8746.436
Q5_Statistician                                                      -1242.9406   6666.697     -0.186      0.852   -1.43e+04    1.18e+04
Q7_Part_1_Python                                                      -350.7443   3037.185     -0.115      0.908   -6308.053    5606.564
Q7_Part_2_R                                                          -2951.3041   2550.556     -1.157      0.247   -7954.111    2051.503
Q7_Part_3_SQL                                                          511.7946   2409.737      0.212      0.832   -4214.802    5238.391
Q7_Part_4_C                                                           3631.3520   3797.227      0.956      0.339   -3816.746    1.11e+04
Q7_Part_5_C++                                                          747.8751   3596.986      0.208      0.835   -6307.459    7803.209
Q7_Part_6_Java                                                        -117.4515   3378.675     -0.035      0.972   -6744.578    6509.675
Q7_Part_7_Javascript                                                 -3735.5488   3479.518     -1.074      0.283   -1.06e+04    3089.377
Q7_Part_8_Julia                                                       4.167e+04   1.55e+04      2.691      0.007    1.13e+04     7.2e+04
Q7_Part_9_Swift                                                       1.138e+04   1.65e+04      0.690      0.490    -2.1e+04    4.37e+04
Q7_Part_10_Bash                                                       8793.7454   4435.378      1.983      0.048      93.941    1.75e+04
Q7_Part_11_MATLAB                                                    -9975.0989   3392.317     -2.940      0.003   -1.66e+04   -3321.215
Q7_Part_12_None                                                       -1.92e+04   1.03e+04     -1.863      0.063   -3.94e+04    1010.236
Q3_Argentina                                                         -1.237e+04    1.3e+04     -0.951      0.342   -3.79e+04    1.31e+04
Q3_Australia                                                          5.639e+04   1.36e+04      4.141      0.000    2.97e+04    8.31e+04
Q3_Bangladesh                                                        -1.869e+04    2.5e+04     -0.747      0.455   -6.77e+04    3.04e+04
Q3_Belarus                                                           -5627.2862   1.96e+04     -0.287      0.774   -4.41e+04    3.28e+04
Q3_Belgium                                                           -1556.0862   1.96e+04     -0.080      0.937   -3.99e+04    3.68e+04
Q3_Brazil                                                            -1.907e+04   7487.072     -2.548      0.011   -3.38e+04   -4387.965
Q3_Canada                                                             3.929e+04   7997.887      4.912      0.000    2.36e+04     5.5e+04
Q3_Chile                                                             -1.023e+04   1.68e+04     -0.611      0.542   -4.31e+04    2.26e+04
Q3_China                                                              4.215e+04   1.28e+04      3.298      0.001    1.71e+04    6.72e+04
Q3_Colombia                                                            -2.2e+04   1.36e+04     -1.620      0.105   -4.86e+04    4631.461
Q3_Egypt                                                             -1.558e+04   1.04e+04     -1.492      0.136   -3.61e+04    4909.550
Q3_France                                                                     0   8875.153          0      1.000   -1.74e+04    1.74e+04
Q3_Germany                                                            3.015e+04   8539.358      3.531      0.000    1.34e+04    4.69e+04
Q3_Ghana                                                             -1.107e+04   2.18e+04     -0.508      0.612   -5.39e+04    3.17e+04
Q3_Greece                                                                     0   1.57e+04          0      1.000   -3.08e+04    3.08e+04
Q3_India                                                             -1.598e+04   4780.113     -3.343      0.001   -2.54e+04   -6603.555
Q3_Indonesia                                                         -3832.6656   8621.604     -0.445      0.657   -2.07e+04    1.31e+04
Q3_Iran, Islamic Republic of...                                      -1.371e+04   1.07e+04     -1.276      0.202   -3.48e+04    7367.452
Q3_Ireland                                                            1.155e+04   1.26e+04      0.920      0.358   -1.31e+04    3.62e+04
Q3_Israel                                                             2.377e+04   1.66e+04      1.429      0.153   -8848.791    5.64e+04
Q3_Italy                                                                      0   1.02e+04          0      1.000   -2.01e+04    2.01e+04
Q3_Japan                                                               1.75e+04   1.05e+04      1.661      0.097   -3167.008    3.82e+04
Q3_Kenya                                                             -8354.6166   1.41e+04     -0.591      0.555   -3.61e+04    1.94e+04
Q3_Malaysia                                                          -1.543e+04    1.1e+04     -1.408      0.159   -3.69e+04    6061.673
Q3_Mexico                                                            -1.334e+04   1.22e+04     -1.094      0.274   -3.72e+04    1.06e+04
Q3_Morocco                                                           -9146.4432   1.43e+04     -0.640      0.522   -3.72e+04    1.89e+04
Q3_Nepal                                                             -1510.1364   4.28e+04     -0.035      0.972   -8.55e+04    8.25e+04
Q3_Netherlands                                                        3.937e+04   1.12e+04      3.518      0.000    1.74e+04    6.13e+04
Q3_Nigeria                                                           -1605.1449   7964.672     -0.202      0.840   -1.72e+04     1.4e+04
Q3_Pakistan                                                          -1.277e+04   1.05e+04     -1.219      0.223   -3.33e+04    7784.302
Q3_Peru                                                              -1.304e+04   1.41e+04     -0.923      0.356   -4.07e+04    1.47e+04
Q3_Philippines                                                       -1.396e+04   1.26e+04     -1.109      0.267   -3.86e+04    1.07e+04
Q3_Poland                                                            -8176.3336   1.31e+04     -0.626      0.531   -3.38e+04    1.74e+04
Q3_Portugal                                                                   0   1.01e+04          0      1.000   -1.97e+04    1.97e+04
Q3_Republic of Korea                                                 -3.065e+04   3.05e+04     -1.006      0.315   -9.04e+04    2.91e+04
Q3_Romania                                                           -1.357e+04   1.69e+04     -0.804      0.422   -4.67e+04    1.96e+04
Q3_Russia                                                            -9992.5948   8234.403     -1.214      0.225   -2.61e+04    6158.833
Q3_Saudi Arabia                                                      -1.591e+04   1.48e+04     -1.076      0.282   -4.49e+04    1.31e+04
Q3_Singapore                                                          1.624e+04   1.18e+04      1.375      0.169   -6932.531    3.94e+04
Q3_South Africa                                                      -1.474e+04   1.36e+04     -1.085      0.278   -4.14e+04    1.19e+04
Q3_South Korea                                                        -356.3269   1.26e+04     -0.028      0.978   -2.51e+04    2.44e+04
Q3_Spain                                                             -8469.1964   9010.309     -0.940      0.347   -2.61e+04    9204.137
Q3_Sri Lanka                                                         -1.605e+04   1.42e+04     -1.134      0.257   -4.38e+04    1.17e+04
Q3_Sweden                                                             1.644e+04   1.57e+04      1.045      0.296   -1.44e+04    4.73e+04
Q3_Switzerland                                                        8.221e+04   1.57e+04      5.233      0.000    5.14e+04    1.13e+05
Q3_Taiwan                                                            -1097.3784   1.18e+04     -0.093      0.926   -2.43e+04    2.21e+04
Q3_Thailand                                                          -3964.0245   1.27e+04     -0.313      0.754   -2.88e+04    2.09e+04
Q3_Tunisia                                                           -9682.4769   1.04e+04     -0.933      0.351      -3e+04    1.07e+04
Q3_Turkey                                                             -1.53e+04   9703.819     -1.577      0.115   -3.43e+04    3731.810
Q3_Ukraine                                                           -6711.1602   1.25e+04     -0.536      0.592   -3.13e+04    1.79e+04
Q3_United Arab Emirates                                              -1369.2247   1.95e+04     -0.070      0.944   -3.96e+04    3.69e+04
Q3_United Kingdom of Great Britain and Northern Ireland               1.846e+04   7068.273      2.612      0.009    4598.307    3.23e+04
Q3_United States of America                                           6.793e+04   5002.745     13.578      0.000    5.81e+04    7.77e+04
Q3_Viet Nam                                                          -7301.3795    1.3e+04     -0.562      0.574   -3.28e+04    1.82e+04
Q6_1-2 years                                                          1280.2710   5193.347      0.247      0.805   -8906.256    1.15e+04
Q6_10-20 years                                                        2.457e+04   5922.044      4.149      0.000     1.3e+04    3.62e+04
Q6_20+ years                                                          1.149e+04   6892.822      1.667      0.096   -2027.136     2.5e+04
Q6_3-5 years                                                                  0   5217.486          0      1.000   -1.02e+04    1.02e+04
Q6_5-10 years                                                         9523.1238   5485.250      1.736      0.083   -1235.958    2.03e+04
Q6_< 1 years                                                           411.6425   5432.340      0.076      0.940   -1.02e+04    1.11e+04
Q20_0-49 employees                                                   -1.337e+04   3260.910     -4.100      0.000   -1.98e+04   -6973.835
Q20_10,000 or more employees                                          8450.7741   3809.803      2.218      0.027     978.008    1.59e+04
Q20_1000-9,999 employees                                                      0   3819.848          0      1.000   -7492.468    7492.468
Q20_250-999 employees                                                 3166.2161   3916.913      0.808      0.419   -4516.640    1.08e+04
==============================================================================
Omnibus:                     1736.731   Durbin-Watson:                   1.998
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           156588.973
Skew:                           4.832   Prob(JB):                         0.00
Kurtosis:                      49.256   Cond. No.                         72.6
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.


# create and train men's model 
men_fin = qnums(['Q4', 'Q5', 'Q7', 'Q3', 'Q6', 'Q20'], Men_Model)
Y_M = Men_Model.aprox_salary
X_M = men_fin.drop(['Q4_I prefer not to answer', 'Q5_Other', 
                  'Q7_OTHER_Other', 'Q3_Other', 'Q6_I have never written code', 'Q20_50-249 employees'], axis=1)
X_M = sm.add_constant(X_M)

model_M = sm.OLS(Y_M, X_M)
results_M = model_M.fit()
results_M.summary()


results_reg_M = model_M.fit_regularized(L1_wt=1, alpha= 5)
final_M = sm.regression.linear_model.OLSResults(model_M, results_reg_M.params, model_M.normalized_cov_params)
print(final_M.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:           aprox_salary   R-squared:                       0.384
Model:                            OLS   Adj. R-squared:                  0.378
Method:                 Least Squares   F-statistic:                     59.54
Date:                Wed, 27 Jul 2022   Prob (F-statistic):               0.00
Time:                        15:55:33   Log-Likelihood:            -1.0892e+05
No. Observations:                8872   AIC:                         2.180e+05
Df Residuals:                    8779   BIC:                         2.187e+05
Df Model:                          92                                         
Covariance Type:            nonrobust                                         
========================================================================================================================================
                                                                           coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------------------------------------------------------------
const                                                                 3.468e+04   5932.655      5.846      0.000    2.31e+04    4.63e+04
Q4_Bachelor’s degree                                                          0   4893.868          0      1.000   -9593.128    9593.128
Q4_Doctoral degree                                                    8264.3993   5074.562      1.629      0.103   -1682.931    1.82e+04
Q4_Master’s degree                                                    3271.3199   4858.931      0.673      0.501   -6253.324    1.28e+04
Q4_No formal education past high school                              -6537.4051   7075.537     -0.924      0.356   -2.04e+04    7332.304
Q4_Professional degree                                                6278.9759   5478.656      1.146      0.252   -4460.474     1.7e+04
Q4_Some college/university study without earning a bachelor’s degree -2260.4077   5559.604     -0.407      0.684   -1.32e+04    8637.719
Q5_Business Analyst                                                  -1998.0261   2723.539     -0.734      0.463   -7336.800    3340.747
Q5_DBA/Database Engineer                                             -8095.6059   5672.153     -1.427      0.154   -1.92e+04    3023.143
Q5_Data Analyst                                                      -9735.0570   2304.354     -4.225      0.000   -1.43e+04   -5217.983
Q5_Data Engineer                                                     -4610.5224   3381.361     -1.364      0.173   -1.12e+04    2017.737
Q5_Data Scientist                                                     4763.8449   1988.270      2.396      0.017     866.370    8661.320
Q5_Machine Learning Engineer                                          2424.3817   2474.172      0.980      0.327   -2425.574    7274.338
Q5_Product/Project Manager                                            9614.6890   2765.632      3.476      0.001    4193.403     1.5e+04
Q5_Research Scientist                                                -7804.6728   2557.338     -3.052      0.002   -1.28e+04   -2791.691
Q5_Software Engineer                                                 -5616.1137   2227.651     -2.521      0.012   -9982.831   -1249.396
Q5_Statistician                                                      -5766.4457   4202.386     -1.372      0.170    -1.4e+04    2471.216
Q7_Part_1_Python                                                      3770.5574   1807.305      2.086      0.037     227.816    7313.299
Q7_Part_2_R                                                            563.6165   1410.387      0.400      0.689   -2201.071    3328.304
Q7_Part_3_SQL                                                         1653.6484   1263.062      1.309      0.190    -822.250    4129.546
Q7_Part_4_C                                                           -113.3245   1900.007     -0.060      0.952   -3837.783    3611.134
Q7_Part_5_C++                                                        -2430.9730   1743.590     -1.394      0.163   -5848.819     986.873
Q7_Part_6_Java                                                         231.6885   1665.871      0.139      0.889   -3033.810    3497.187
Q7_Part_7_Javascript                                                 -2995.9778   1636.368     -1.831      0.067   -6203.643     211.687
Q7_Part_8_Julia                                                       6852.0547   4217.055      1.625      0.104   -1414.361    1.51e+04
Q7_Part_9_Swift                                                       4880.6944   4904.011      0.995      0.320   -4732.315    1.45e+04
Q7_Part_10_Bash                                                       7241.8094   1713.015      4.228      0.000    3883.898    1.06e+04
Q7_Part_11_MATLAB                                                    -4363.9347   1979.056     -2.205      0.027   -8243.349    -484.521
Q7_Part_12_None                                                      -3684.8010   6143.625     -0.600      0.549   -1.57e+04    8358.144
Q3_Argentina                                                          -2.28e+04   6627.953     -3.440      0.001   -3.58e+04   -9804.740
Q3_Australia                                                          4.531e+04   5051.402      8.970      0.000    3.54e+04    5.52e+04
Q3_Bangladesh                                                        -1.856e+04   8245.491     -2.251      0.024   -3.47e+04   -2399.743
Q3_Belarus                                                           -1.981e+04   9477.161     -2.091      0.037   -3.84e+04   -1236.754
Q3_Belgium                                                            1.576e+04   9777.423      1.612      0.107   -3404.450    3.49e+04
Q3_Brazil                                                            -1.646e+04   3326.640     -4.947      0.000    -2.3e+04   -9935.268
Q3_Canada                                                             3.155e+04   4656.782      6.775      0.000    2.24e+04    4.07e+04
Q3_Chile                                                             -1.002e+04   7351.459     -1.363      0.173   -2.44e+04    4394.150
Q3_China                                                                      0   4761.787          0      1.000   -9334.218    9334.218
Q3_Colombia                                                           -2.16e+04   5411.474     -3.991      0.000   -3.22e+04    -1.1e+04
Q3_Egypt                                                             -1.671e+04   6543.622     -2.553      0.011   -2.95e+04   -3882.060
Q3_France                                                             5458.2950   4590.035      1.189      0.234   -3539.249    1.45e+04
Q3_Germany                                                            2.831e+04   4115.214      6.879      0.000    2.02e+04    3.64e+04
Q3_Ghana                                                                      0   1.29e+04          0      1.000   -2.52e+04    2.52e+04
Q3_Greece                                                            -1.322e+04   7224.040     -1.830      0.067   -2.74e+04     940.223
Q3_India                                                             -1.443e+04   2406.524     -5.995      0.000   -1.91e+04   -9708.818
Q3_Indonesia                                                         -9261.6153   5922.366     -1.564      0.118   -2.09e+04    2347.610
Q3_Iran, Islamic Republic of...                                      -2.696e+04   7183.472     -3.753      0.000    -4.1e+04   -1.29e+04
Q3_Ireland                                                            1.652e+04   1.16e+04      1.424      0.155   -6224.274    3.93e+04
Q3_Israel                                                             7.234e+04   7348.087      9.844      0.000    5.79e+04    8.67e+04
Q3_Italy                                                             -4654.4396   4603.029     -1.011      0.312   -1.37e+04    4368.575
Q3_Japan                                                                      0   3458.663          0      1.000   -6779.790    6779.790
Q3_Kenya                                                             -1.658e+04   6998.134     -2.370      0.018   -3.03e+04   -2866.607
Q3_Malaysia                                                          -6500.4962   8966.736     -0.725      0.468   -2.41e+04    1.11e+04
Q3_Mexico                                                            -1.351e+04   5144.463     -2.626      0.009   -2.36e+04   -3423.889
Q3_Morocco                                                           -1.785e+04   7726.225     -2.310      0.021    -3.3e+04   -2703.521
Q3_Nepal                                                             -8436.9915   1.22e+04     -0.692      0.489   -3.23e+04    1.54e+04
Q3_Netherlands                                                        2.357e+04   5860.664      4.021      0.000    1.21e+04    3.51e+04
Q3_Nigeria                                                           -1.805e+04   4234.222     -4.264      0.000   -2.64e+04   -9754.066
Q3_Pakistan                                                          -1.472e+04   5610.908     -2.623      0.009   -2.57e+04   -3717.137
Q3_Peru                                                              -1.593e+04   7542.678     -2.113      0.035   -3.07e+04   -1149.421
Q3_Philippines                                                       -1.407e+04   8967.652     -1.568      0.117   -3.16e+04    3513.640
Q3_Poland                                                            -9419.0895   6064.639     -1.553      0.120   -2.13e+04    2469.023
Q3_Portugal                                                            -1.2e+04   6866.826     -1.748      0.081   -2.55e+04    1459.529
Q3_Republic of Korea                                                          0   8531.707          0      1.000   -1.67e+04    1.67e+04
Q3_Romania                                                           -1.475e+04   9628.549     -1.532      0.126   -3.36e+04    4124.927
Q3_Russia                                                            -1.623e+04   3614.826     -4.489      0.000   -2.33e+04   -9142.232
Q3_Saudi Arabia                                                               0   8741.415          0      1.000   -1.71e+04    1.71e+04
Q3_Singapore                                                           1.75e+04   6506.101      2.690      0.007    4745.348    3.03e+04
Q3_South Africa                                                               0   6662.449          0      1.000   -1.31e+04    1.31e+04
Q3_South Korea                                                        6904.1116   6427.526      1.074      0.283   -5695.346    1.95e+04
Q3_Spain                                                                      0   4196.730          0      1.000   -8226.575    8226.575
Q3_Sri Lanka                                                         -1.331e+04   1.01e+04     -1.316      0.188   -3.31e+04    6511.672
Q3_Sweden                                                             1.167e+04   7988.214      1.461      0.144   -3990.812    2.73e+04
Q3_Switzerland                                                        7.296e+04   8536.824      8.547      0.000    5.62e+04    8.97e+04
Q3_Taiwan                                                            -1.232e+04   5474.407     -2.251      0.024   -2.31e+04   -1593.680
Q3_Thailand                                                          -2772.8678   7121.235     -0.389      0.697   -1.67e+04    1.12e+04
Q3_Tunisia                                                           -3333.6565   9939.030     -0.335      0.737   -2.28e+04    1.61e+04
Q3_Turkey                                                            -1.763e+04   4892.042     -3.605      0.000   -2.72e+04   -8043.898
Q3_Ukraine                                                           -1.466e+04   5639.376     -2.600      0.009   -2.57e+04   -3608.107
Q3_United Arab Emirates                                               3.039e+04   8521.341      3.567      0.000    1.37e+04    4.71e+04
Q3_United Kingdom of Great Britain and Northern Ireland               4.105e+04   3718.650     11.039      0.000    3.38e+04    4.83e+04
Q3_United States of America                                           7.705e+04   2570.367     29.974      0.000     7.2e+04    8.21e+04
Q3_Viet Nam                                                          -1.808e+04   6557.131     -2.757      0.006   -3.09e+04   -5227.686
Q6_1-2 years                                                         -1.276e+04   3448.824     -3.700      0.000   -1.95e+04   -5999.140
Q6_10-20 years                                                        1.391e+04   3571.346      3.894      0.000    6907.881    2.09e+04
Q6_20+ years                                                           2.35e+04   3674.774      6.395      0.000    1.63e+04    3.07e+04
Q6_3-5 years                                                         -9566.3037   3413.151     -2.803      0.005   -1.63e+04   -2875.728
Q6_5-10 years                                                                 0   3487.178          0      1.000   -6835.686    6835.686
Q6_< 1 years                                                         -1.233e+04   3526.267     -3.498      0.000   -1.92e+04   -5421.619
Q20_0-49 employees                                                   -8103.4404   1742.360     -4.651      0.000   -1.15e+04   -4688.006
Q20_10,000 or more employees                                           1.05e+04   1945.990      5.395      0.000    6683.776    1.43e+04
Q20_1000-9,999 employees                                              5315.3648   1979.819      2.685      0.007    1434.455    9196.274
Q20_250-999 employees                                                         0   2190.298          0      1.000   -4293.497    4293.497
==============================================================================
Omnibus:                     7603.734   Durbin-Watson:                   2.000
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           317591.903
Skew:                           3.939   Prob(JB):                         0.00
Kurtosis:                      31.232   Cond. No.                         52.5
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.


# run model on all data & compare. Keep in mind that the data should have been split into train and test set in order to
# compareresults effectively. This was not done here.
combined_data = qnums(['Q4', 'Q5', 'Q7', 'Q3', 'Q6', 'Q20'], model_dummies).drop(['Q4_I prefer not to answer', 
                                                                                  'Q5_Other', 'Q7_OTHER_Other', 'Q3_Other', 
                                                                                  'Q6_I have never written code', 
                                                                                  'Q20_50-249 employees'], axis=1)
male_preds = final_M.predict(np.array(sm.add_constant(combined_data)))
female_preds = final_W.predict(np.array(sm.add_constant(combined_data)))


# add prediction values to the data
combined_data['male_preds'] = male_preds
combined_data['female_preds'] = female_preds


px.scatter(combined_data, x='male_preds', y='female_preds', trendline='ols', trendline_color_override='red')


# combine with the approximate salary column and compare with predictor columns.
combined_data['aprox_salary'] = model_dummies.aprox_salary
combined_data.head()


# looking at the male and female predictor predictions at the different apporximate salaries
px.scatter(combined_data.sort_values('aprox_salary'), x = 'aprox_salary', y = ['male_preds','female_preds'])


# looking at the difference in salaries between the male perdictions and female predictions
combined_data['projected_diff'] = combined_data.male_preds - combined_data.female_preds


combined_data.projected_diff.mean()

10059.919985464097


combined_data.projected_diff.std()

15363.821440407619


# converting negative values to 1 and positive values to 0 for easy comparison
combined_data['women_prj_higher'] = combined_data.projected_diff.apply(lambda x: 1 if x < 0 else 0)


# here, we see that the female predictor predicted a higher salary than the male predictor about a quarter of the time.
combined_data.women_prj_higher.value_counts()

0    8344
1    2211
Name: women_prj_higher, dtype: int64

	Time from Start to Finish (seconds)	Q1	Q2	Q3	Q4	Q5	Q6	Q7_Part_1	Q7_Part_2	Q7_Part_3	Q7_Part_4	Q7_Part_5	Q7_Part_6	Q7_Part_7	Q7_Part_8	Q7_Part_9	Q7_Part_10	Q7_Part_11	Q7_Part_12	Q7_OTHER	Q8	Q9_Part_1	Q9_Part_2	Q9_Part_3	Q9_Part_4	Q9_Part_5	Q9_Part_6	Q9_Part_7	Q9_Part_8	Q9_Part_9	Q9_Part_10	Q9_Part_11	Q9_OTHER	Q10_Part_1	Q10_Part_2	Q10_Part_3	Q10_Part_4	Q10_Part_5	Q10_Part_6	Q10_Part_7	Q10_Part_8	Q10_Part_9	Q10_Part_10	Q10_Part_11	Q10_Part_12	Q10_Part_13	Q10_OTHER	Q11	Q12_Part_1	Q12_Part_2	Q12_Part_3	Q12_OTHER	Q13	Q14_Part_1	Q14_Part_2	Q14_Part_3	Q14_Part_4	Q14_Part_5	Q14_Part_6	Q14_Part_7	Q14_Part_8	Q14_Part_9	Q14_Part_10	Q14_Part_11	Q14_OTHER	Q15	Q16_Part_1	Q16_Part_2	Q16_Part_3	Q16_Part_4	Q16_Part_5	Q16_Part_6	Q16_Part_7	Q16_Part_8	Q16_Part_9	Q16_Part_10	Q16_Part_11	Q16_Part_12	Q16_Part_13	Q16_Part_14	Q16_Part_15	Q16_OTHER	Q17_Part_1	Q17_Part_2	Q17_Part_3	Q17_Part_4	Q17_Part_5	Q17_Part_6	Q17_Part_7	Q17_Part_8	Q17_Part_9	Q17_Part_10	Q17_Part_11	Q17_OTHER	Q18_Part_1	Q18_Part_2	Q18_Part_3	Q18_Part_4	Q18_Part_5	Q18_Part_6	Q18_OTHER	Q19_Part_1	Q19_Part_2	Q19_Part_3	Q19_Part_4	Q19_Part_5	Q19_OTHER	Q20	Q21	Q22	Q23_Part_1	Q23_Part_2	Q23_Part_3	Q23_Part_4	Q23_Part_5	Q23_Part_6	Q23_Part_7	Q23_OTHER	Q24	Q25	Q26_A_Part_1	Q26_A_Part_2	Q26_A_Part_3	Q26_A_Part_4	Q26_A_Part_5	Q26_A_Part_6	Q26_A_Part_7	Q26_A_Part_8	Q26_A_Part_9	Q26_A_Part_10	Q26_A_Part_11	Q26_A_OTHER	Q27_A_Part_1	Q27_A_Part_2	Q27_A_Part_3	Q27_A_Part_4	Q27_A_Part_5	Q27_A_Part_6	Q27_A_Part_7	Q27_A_Part_8	Q27_A_Part_9	Q27_A_Part_10	Q27_A_Part_11	Q27_A_OTHER	Q28_A_Part_1	Q28_A_Part_2	Q28_A_Part_3	Q28_A_Part_4	Q28_A_Part_5	Q28_A_Part_6	Q28_A_Part_7	Q28_A_Part_8	Q28_A_Part_9	Q28_A_Part_10	Q28_A_OTHER	Q29_A_Part_1	Q29_A_Part_2	Q29_A_Part_3	Q29_A_Part_4	Q29_A_Part_5	Q29_A_Part_6	Q29_A_Part_7	Q29_A_Part_8	Q29_A_Part_9	Q29_A_Part_10	Q29_A_Part_11	Q29_A_Part_12	Q29_A_Part_13	Q29_A_Part_14	Q29_A_Part_15	Q29_A_Part_16	Q29_A_Part_17	Q29_A_OTHER	Q30	Q31_A_Part_1	Q31_A_Part_2	Q31_A_Part_3	Q31_A_Part_4	Q31_A_Part_5	Q31_A_Part_6	Q31_A_Part_7	Q31_A_Part_8	Q31_A_Part_9	Q31_A_Part_10	Q31_A_Part_11	Q31_A_Part_12	Q31_A_Part_13	Q31_A_Part_14	Q31_A_OTHER	Q32	Q33_A_Part_1	Q33_A_Part_2	Q33_A_Part_3	Q33_A_Part_4	Q33_A_Part_5	Q33_A_Part_6	Q33_A_Part_7	Q33_A_OTHER	Q34_A_Part_1	Q34_A_Part_2	Q34_A_Part_3	Q34_A_Part_4	Q34_A_Part_5	Q34_A_Part_6	Q34_A_Part_7	Q34_A_Part_8	Q34_A_Part_9	Q34_A_Part_10	Q34_A_Part_11	Q34_A_OTHER	Q35_A_Part_1	Q35_A_Part_2	Q35_A_Part_3	Q35_A_Part_4	Q35_A_Part_5	Q35_A_Part_6	Q35_A_Part_7	Q35_A_Part_8	Q35_A_Part_9	Q35_A_Part_10	Q35_A_OTHER	Q36_Part_1	Q36_Part_2	Q36_Part_3	Q36_Part_4	Q36_Part_5	Q36_Part_6	Q36_Part_7	Q36_Part_8	Q36_Part_9	Q36_OTHER	Q37_Part_1	Q37_Part_2	Q37_Part_3	Q37_Part_4	Q37_Part_5	Q37_Part_6	Q37_Part_7	Q37_Part_8	Q37_Part_9	Q37_Part_10	Q37_Part_11	Q37_OTHER	Q38	Q39_Part_1	Q39_Part_2	Q39_Part_3	Q39_Part_4	Q39_Part_5	Q39_Part_6	Q39_Part_7	Q39_Part_8	Q39_Part_9	Q39_Part_10	Q39_Part_11	Q39_OTHER	Q26_B_Part_1	Q26_B_Part_2	Q26_B_Part_3	Q26_B_Part_4	Q26_B_Part_5	Q26_B_Part_6	Q26_B_Part_7	Q26_B_Part_8	Q26_B_Part_9	Q26_B_Part_10	Q26_B_Part_11	Q26_B_OTHER	Q27_B_Part_1	Q27_B_Part_2	Q27_B_Part_3	Q27_B_Part_4	Q27_B_Part_5	Q27_B_Part_6	Q27_B_Part_7	Q27_B_Part_8	Q27_B_Part_9	Q27_B_Part_10	Q27_B_Part_11	Q27_B_OTHER	Q28_B_Part_1	Q28_B_Part_2	Q28_B_Part_3	Q28_B_Part_4	Q28_B_Part_5	Q28_B_Part_6	Q28_B_Part_7	Q28_B_Part_8	Q28_B_Part_9	Q28_B_Part_10	Q28_B_OTHER	Q29_B_Part_1	Q29_B_Part_2	Q29_B_Part_3	Q29_B_Part_4	Q29_B_Part_5	Q29_B_Part_6	Q29_B_Part_7	Q29_B_Part_8	Q29_B_Part_9	Q29_B_Part_10	Q29_B_Part_11	Q29_B_Part_12	Q29_B_Part_13	Q29_B_Part_14	Q29_B_Part_15	Q29_B_Part_16	Q29_B_Part_17	Q29_B_OTHER	Q31_B_Part_1	Q31_B_Part_2	Q31_B_Part_3	Q31_B_Part_4	Q31_B_Part_5	Q31_B_Part_6	Q31_B_Part_7	Q31_B_Part_8	Q31_B_Part_9	Q31_B_Part_10	Q31_B_Part_11	Q31_B_Part_12	Q31_B_Part_13	Q31_B_Part_14	Q31_B_OTHER	Q33_B_Part_1	Q33_B_Part_2	Q33_B_Part_3	Q33_B_Part_4	Q33_B_Part_5	Q33_B_Part_6	Q33_B_Part_7	Q33_B_OTHER	Q34_B_Part_1	Q34_B_Part_2	Q34_B_Part_3	Q34_B_Part_4	Q34_B_Part_5	Q34_B_Part_6	Q34_B_Part_7	Q34_B_Part_8	Q34_B_Part_9	Q34_B_Part_10	Q34_B_Part_11	Q34_B_OTHER	Q35_B_Part_1	Q35_B_Part_2	Q35_B_Part_3	Q35_B_Part_4	Q35_B_Part_5	Q35_B_Part_6	Q35_B_Part_7	Q35_B_Part_8	Q35_B_Part_9	Q35_B_Part_10	Q35_B_OTHER
0	Duration (in seconds)	What is your age (# years)?	What is your gender? - Selected Choice	In which country do you currently reside?	What is the highest level of formal education ...	Select the title most similar to your current ...	For how many years have you been writing code ...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming languages do you use on a reg...	What programming language would you recommend ...	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Do you use any of the following cloud computin...	Do you use any of the following cloud computin...	Do you use any of the following cloud computin...	Do you use any of the following machine learni...	Do you use any of the following machine learni...	Do you use any of the following machine learni...	Do you use any of the following machine learni...	Do you use any of the following machine learni...	Do you use any of the following machine learni...	Do you use any of the following machine learni...	Do you use any of the following machine learni...	Do you use any of the following machine learni...	Do you use any of the following machine learni...	Do you use any of the following machine learni...	Which of the following big data products (rela...	Which of the following big data products (rela...	Which of the following big data products (rela...	Which of the following big data products (rela...	Which of the following big data products (rela...	Which of the following big data products (rela...	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1	1838	35-39	Man	Colombia	Doctoral degree	Student	5-10 years	Python	R	SQL	C	NaN	NaN	Javascript	NaN	NaN	NaN	MATLAB	NaN	Other	Python	Jupyter (JupyterLab, Jupyter Notebooks, etc)	NaN	NaN	Visual Studio Code (VSCode)	NaN	Spyder	NaN	NaN	NaN	NaN	NaN	NaN	Kaggle Notebooks	Colab Notebooks	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	A cloud computing platform (AWS, Azure, GCP, h...	GPUs	NaN	NaN	NaN	2-5 times	Matplotlib	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Geoplotlib	NaN	NaN	NaN	1-2 years	NaN	TensorFlow	Keras	NaN	NaN	NaN	Xgboost	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Decision Trees or Random Forests	Gradient Boosting Machines (xgboost, lightgbm,...	Bayesian Approaches	NaN	Dense Neural Networks (MLPs, etc)	Convolutional Neural Networks	NaN	Recurrent Neural Networks	NaN	NaN	NaN	NaN	NaN	NaN	Image classification and other general purpose...	NaN	NaN	NaN	Word embeddings/vectors (GLoVe, fastText, word...	NaN	Contextualized embeddings (ELMo, CoVe)	Transformer language models (GPT-3, BERT, XLne...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Coursera	NaN	Kaggle Learn Courses	NaN	NaN	NaN	NaN	NaN	NaN	University Courses (resulting in a university ...	NaN	NaN	Basic statistical software (Microsoft Excel, G...	NaN	NaN	NaN	Kaggle (notebooks, forums, etc)	NaN	NaN	NaN	NaN	Journal Publications (peer-reviewed journals, ...	NaN	NaN	NaN	Amazon Web Services (AWS)	Microsoft Azure	Google Cloud Platform (GCP)	IBM Cloud / Red Hat	NaN	SAP Cloud	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Azure Cloud Services	Microsoft Azure Container Instances	Azure Functions	Google Cloud Compute Engine	Google Cloud Functions	Google Cloud Run	Google Cloud App Engine	NaN	NaN	Amazon SageMaker	Amazon Forecast	Amazon Rekognition	Azure Machine Learning Studio	Azure Cognitive Services	Google Cloud AI Platform / Google Cloud ML En...	Google Cloud Video AI	Google Cloud Natural Language	Google Cloud Vision AI	NaN	NaN	NaN	NaN	NaN	NaN	MongoDB	NaN	NaN	Microsoft SQL Server	NaN	NaN	NaN	NaN	NaN	Google Cloud BigQuery	Google Cloud SQL	Google Cloud Firestore	NaN	NaN	Microsoft Power BI	Amazon QuickSight	Google Data Studio	NaN	Tableau	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SAP Analytics Cloud	NaN	NaN	Automated data augmentation (e.g. imgaug, albu...	NaN	NaN	NaN	Automated hyperparameter tuning (e.g. hyperopt...	Automation of full ML pipelines (e.g. Google C...	NaN	NaN	Google Cloud AutoML	NaN	Databricks AutoML	NaN	NaN	Auto-Keras	Auto-Sklearn	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	TensorBoard	NaN	NaN	NaN	NaN	NaN	NaN
2	289287	30-34	Man	United States of America	Master’s degree	Data Engineer	5-10 years	Python	R	SQL	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Python	NaN	NaN	Visual Studio	NaN	PyCharm	NaN	NaN	Sublime Text	NaN	NaN	NaN	NaN	NaN	Colab Notebooks	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	A personal computer or laptop	GPUs	NaN	NaN	NaN	2-5 times	Matplotlib	Seaborn	NaN	Ggplot / ggplot2	Shiny	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1-2 years	Scikit-learn	TensorFlow	Keras	PyTorch	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Linear or Logistic Regression	NaN	NaN	NaN	NaN	NaN	Convolutional Neural Networks	NaN	NaN	Transformer Networks (BERT, gpt-3, etc)	NaN	NaN	NaN	Image segmentation methods (U-Net, Mask R-CNN,...	NaN	Image classification and other general purpose...	NaN	NaN	NaN	NaN	NaN	Contextualized embeddings (ELMo, CoVe)	Transformer language models (GPT-3, BERT, XLne...	NaN	NaN	10,000 or more employees	20+	We have well established ML methods (i.e., mod...	Analyze and understand data to influence produ...	NaN	NaN	NaN	NaN	Do research that advances the state of the art...	NaN	NaN	100,000-124,999	$100,000 or more ($USD)	Amazon Web Services (AWS)	Microsoft Azure	Google Cloud Platform (GCP)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Amazon EC2	AWS Lambda	NaN	NaN	NaN	Azure Functions	Google Cloud Compute Engine	NaN	NaN	NaN	NaN	NaN	Amazon SageMaker	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	PostgresSQL	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Amazon Redshift	Amazon Athena	NaN	NaN	NaN	NaN	NaN	NaN	PostgresSQL	Amazon QuickSight	Microsoft Power BI	NaN	NaN	Tableau	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Microsoft Power BI	NaN	NaN	NaN	NaN	NaN	NaN	No / None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	No / None	NaN	NaN	NaN	NaN	GitHub	NaN	NaN	NaN	NaN	NaN	NaN	Coursera	NaN	NaN	DataCamp	NaN	NaN	Udemy	NaN	NaN	NaN	NaN	NaN	Business intelligence software (Salesforce, Ta...	Twitter (data science influencers)	NaN	Reddit (r/machinelearning, etc)	Kaggle (notebooks, forums, etc)	Course Forums (forums.fast.ai, Coursera forums...	YouTube (Kaggle YouTube, Cloud AI Adventures, ...	NaN	Blogs (Towards Data Science, Analytics Vidhya,...	NaN	Slack Communities (ods.ai, kagglenoobs, etc)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	860	35-39	Man	Argentina	Bachelor’s degree	Software Engineer	10-20 years	NaN	NaN	NaN	NaN	NaN	Java	Javascript	NaN	NaN	Bash	NaN	NaN	NaN	R	NaN	NaN	NaN	Visual Studio Code (VSCode)	NaN	NaN	Notepad++	Sublime Text	Vim / Emacs	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN	A personal computer or laptop	NaN	NaN	None	NaN	Never	NaN	NaN	NaN	NaN	NaN	D3 js	NaN	NaN	NaN	NaN	NaN	NaN	I do not use machine learning methods	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1000-9,999 employees	0	No (we do not use ML methods)	NaN	NaN	NaN	NaN	NaN	NaN	None of these activities are an important part...	NaN	15,000-19,999	$0 ($USD)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	MySQL	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	No / None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	No / None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Coursera	edX	NaN	NaN	NaN	Udacity	Udemy	NaN	NaN	NaN	NaN	NaN	Basic statistical software (Microsoft Excel, G...	NaN	Email newsletters (Data Elixir, O'Reilly Data ...	NaN	Kaggle (notebooks, forums, etc)	NaN	YouTube (Kaggle YouTube, Cloud AI Adventures, ...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	MySQL	NaN	NaN	NaN	NaN	NaN	NaN	Microsoft SQL Server	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN
4	507	30-34	Man	United States of America	Master’s degree	Data Scientist	5-10 years	Python	NaN	SQL	NaN	NaN	NaN	NaN	NaN	NaN	Bash	NaN	NaN	NaN	Python	NaN	NaN	NaN	NaN	PyCharm	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN	A cloud computing platform (AWS, Azure, GCP, h...	NaN	NaN	None	NaN	2-5 times	Matplotlib	Seaborn	Plotly / Plotly Express	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3-4 years	Scikit-learn	TensorFlow	Keras	NaN	NaN	NaN	Xgboost	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Linear or Logistic Regression	Decision Trees or Random Forests	Gradient Boosting Machines (xgboost, lightgbm,...	Bayesian Approaches	NaN	Dense Neural Networks (MLPs, etc)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	250-999 employees	5-9	We have well established ML methods (i.e., mod...	Analyze and understand data to influence produ...	NaN	NaN	Build and/or run a machine learning service th...	Experimentation and iteration to improve exist...	NaN	NaN	NaN	125,000-149,999	$10,000-$99,999	Amazon Web Services (AWS)	NaN	NaN	NaN	NaN	NaN	Salesforce Cloud	NaN	NaN	NaN	NaN	NaN	Amazon EC2	AWS Lambda	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	No / None	NaN	MySQL	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Amazon Redshift	NaN	NaN	NaN	NaN	NaN	NaN	NaN	MySQL	NaN	NaN	NaN	NaN	Tableau	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	No / None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	No / None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	I do not share my work publicly	NaN	Coursera	NaN	NaN	DataCamp	NaN	NaN	NaN	LinkedIn Learning	NaN	University Courses (resulting in a university ...	NaN	NaN	Local development environments (RStudio, Jupyt...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	salary_min	salary_max	aprox_salary	Q1_30-34	Q1_35-39	Q2_Man	Q3_Argentina	Q3_Germany	Q3_United States of America	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer	Q6_1-2 years	Q6_10-20 years	Q6_5-10 years	Q7_Part_1_Python	Q7_Part_2_R	Q7_Part_3_SQL	Q7_Part_6_Java	Q7_Part_7_Javascript	Q7_Part_10_Bash	Q8_Python	Q8_R	Q9_Part_1_Jupyter (JupyterLab, Jupyter Notebooks, etc)	Q9_Part_2_ RStudio	Q9_Part_3_Visual Studio	Q9_Part_4_Visual Studio Code (VSCode)	Q9_Part_5_ PyCharm	Q9_Part_7_ Notepad++	Q9_Part_8_ Sublime Text	Q9_Part_9_ Vim / Emacs	Q10_Part_1_ Kaggle Notebooks	Q10_Part_2_Colab Notebooks	Q10_Part_13_None	Q11_A cloud computing platform (AWS, Azure, GCP, hosted notebooks, etc)	Q11_A personal computer or laptop	Q12_Part_1_GPUs	Q12_Part_3_None	Q12_OTHER_Other	Q13_2-5 times	Q13_Never	Q14_Part_1_ Matplotlib	Q14_Part_2_ Seaborn	Q14_Part_3_ Plotly / Plotly Express	Q14_Part_4_ Ggplot / ggplot2	Q14_Part_5_ Shiny	Q14_Part_6_ D3 js	Q14_Part_7_ Altair	Q14_Part_8_ Bokeh	Q15_1-2 years	Q15_2-3 years	Q15_3-4 years	Q15_I do not use machine learning methods	Q15_Under 1 year	Q16_Part_1_ Scikit-learn	Q16_Part_2_ TensorFlow	Q16_Part_3_ Keras	Q16_Part_4_ PyTorch	Q16_Part_7_ Xgboost	Q16_Part_8_ LightGBM	Q16_Part_13_ Tidymodels	Q17_Part_1_Linear or Logistic Regression	Q17_Part_2_Decision Trees or Random Forests	Q17_Part_3_Gradient Boosting Machines (xgboost, lightgbm, etc)	Q17_Part_4_Bayesian Approaches	Q17_Part_6_Dense Neural Networks (MLPs, etc)	Q17_Part_7_Convolutional Neural Networks	Q17_Part_10_Transformer Networks (BERT, gpt-3, etc)	Q18_Part_2_Image segmentation methods (U-Net, Mask R-CNN, etc)	Q18_Part_3_Object detection methods (YOLOv3, RetinaNet, etc)	Q18_Part_4_Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	Q19_Part_3_Contextualized embeddings (ELMo, CoVe)	Q19_Part_4_Transformer language models (GPT-3, BERT, XLnet, etc)	Q20_0-49 employees	Q20_10,000 or more employees	Q20_1000-9,999 employees	Q20_250-999 employees	Q21_0	Q21_1-2	Q21_20+	Q21_5-9	Q22_No (we do not use ML methods)	Q22_We have well established ML methods (i.e., models in production for more than 2 years)	Q22_We use ML methods for generating insights (but do not put working models into production)	Q23_Part_1_Analyze and understand data to influence product or business decisions	Q23_Part_3_Build prototypes to explore applying machine learning to new areas	Q23_Part_4_Build and/or run a machine learning service that operationally improves my product or workflows	Q23_Part_5_Experimentation and iteration to improve existing ML models	Q23_Part_6_Do research that advances the state of the art of machine learning	Q23_Part_7_None of these activities are an important part of my role at work	...	Q29_A_Part_10_Microsoft Azure Data Lake Storage	Q29_A_Part_11_Amazon Redshift	Q29_A_Part_12_Amazon Athena	Q29_A_Part_17_None	Q30_Microsoft Azure Data Lake Storage	Q30_MySQL	Q30_PostgresSQL	Q31_A_Part_1_Amazon QuickSight	Q31_A_Part_2_Microsoft Power BI	Q31_A_Part_5_Tableau	Q31_A_Part_14_None	Q32_Microsoft Power BI	Q33_A_Part_7_No / None	Q35_A_Part_5_ TensorBoard	Q35_A_Part_10_No / None	Q36_Part_4_ GitHub	Q36_Part_9_I do not share my work publicly	Q37_Part_1_Coursera	Q37_Part_2_edX	Q37_Part_4_DataCamp	Q37_Part_5_Fast.ai	Q37_Part_6_Udacity	Q37_Part_7_Udemy	Q37_Part_8_LinkedIn Learning	Q37_Part_10_University Courses (resulting in a university degree)	Q37_Part_11_None	Q38_Basic statistical software (Microsoft Excel, Google Sheets, etc.)	Q38_Business intelligence software (Salesforce, Tableau, Spotfire, etc.)	Q38_Cloud-based data software & APIs (AWS, GCP, Azure, etc.)	Q38_Local development environments (RStudio, JupyterLab, etc.)	Q39_Part_1_Twitter (data science influencers)	Q39_Part_2_Email newsletters (Data Elixir, O'Reilly Data & AI, etc)	Q39_Part_3_Reddit (r/machinelearning, etc)	Q39_Part_4_Kaggle (notebooks, forums, etc)	Q39_Part_5_Course Forums (forums.fast.ai, Coursera forums, etc)	Q39_Part_6_YouTube (Kaggle YouTube, Cloud AI Adventures, etc)	Q39_Part_8_Blogs (Towards Data Science, Analytics Vidhya, etc)	Q39_Part_9_Journal Publications (peer-reviewed journals, conference proceedings, etc)	Q39_Part_10_Slack Communities (ods.ai, kagglenoobs, etc)	Q39_Part_11_None	Q26_B_Part_11_None	Q29_B_Part_1_MySQL	Q29_B_Part_8_Microsoft SQL Server	Q31_B_Part_14_None	Q33_B_Part_7_None	Q35_B_Part_10_None	salary_cleaned_100000-124999	salary_cleaned_125000-149999	salary_cleaned_15000-19999	salary_cleaned_30000-39999	salary_cleaned_70000-79999
2	100000	124999	112499.5	1	0	1	0	0	1	0	0	1	1	0	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	1	0	1	0	1	0	0	1	0	0	1	1	0	0	1	0	1	1	0	1	1	0	0	0	1	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	0	1	1	1	0	1	1	1	0	1	0	0	0	0	1	0	0	1	0	1	0	0	0	1	0	...	0	1	1	0	0	0	1	1	1	1	0	1	1	0	1	1	0	1	0	1	0	0	1	0	0	0	0	1	0	0	1	0	1	1	1	1	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0
3	15000	19999	17499.5	0	1	1	1	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	1	1	0	1	0	0	0	1	0	1	1	1	0	0	1	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	1	...	0	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	1	0	0	1	1	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	0	0	1	1	1	1	1	1	0	0	1	0	0
4	125000	149999	137499.5	1	0	1	0	0	1	0	0	1	0	1	0	0	0	0	1	1	0	1	0	0	1	1	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0	1	0	1	0	1	1	1	0	0	0	0	0	0	0	1	0	0	1	1	1	0	1	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	0	1	1	0	0	...	0	1	0	0	0	1	0	0	0	1	0	0	1	0	1	0	1	1	0	1	0	0	0	1	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0
9	70000	79999	74999.5	0	1	1	0	1	0	0	1	0	0	1	0	0	0	0	1	1	0	1	0	0	1	1	0	1	0	0	0	1	0	1	1	1	1	0	1	0	1	0	0	1	0	1	1	0	0	0	0	1	1	0	1	0	0	0	1	1	1	1	0	1	0	0	0	1	0	0	1	0	1	1	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	1	1	1	0	0	...	1	0	0	0	1	0	0	0	0	0	1	0	1	1	0	1	0	1	0	0	1	1	1	1	0	0	0	0	1	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1
12	30000	39999	34999.5	0	1	1	0	0	1	0	1	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0	...	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	2.689e+04	5480.525	4.906	0.000	1.61e+04	3.76e+04
Q2_Woman	-1.292e+04	1692.183	-7.634	0.000	-1.62e+04	-9601.676
Q4_Bachelor’s degree	1.088e+04	5385.253	2.020	0.043	324.352	2.14e+04
Q4_Doctoral degree	4.388e+04	5530.348	7.935	0.000	3.3e+04	5.47e+04
Q4_Master’s degree	2.513e+04	5338.990	4.707	0.000	1.47e+04	3.56e+04
Q4_No formal education past high school	6736.3251	8115.798	0.830	0.407	-9172.175	2.26e+04
Q4_Professional degree	1.653e+04	6025.209	2.744	0.006	4721.945	2.83e+04
Q4_Some college/university study without earning a bachelor’s degree	1.521e+04	6189.580	2.457	0.014	3073.799	2.73e+04
Q5_Business Analyst	-5416.6046	2953.257	-1.834	0.067	-1.12e+04	372.338
Q5_DBA/Database Engineer	411.9450	6365.478	0.065	0.948	-1.21e+04	1.29e+04
Q5_Data Analyst	-1.509e+04	2436.229	-6.192	0.000	-1.99e+04	-1.03e+04
Q5_Data Engineer	2381.2133	3697.997	0.644	0.520	-4867.561	9629.987
Q5_Data Scientist	4727.5139	2102.637	2.248	0.025	605.948	8849.080
Q5_Machine Learning Engineer	-9499.1658	2671.681	-3.556	0.000	-1.47e+04	-4262.165
Q5_Product/Project Manager	1.477e+04	3102.542	4.761	0.000	8689.266	2.09e+04
Q5_Research Scientist	-1.423e+04	2729.248	-5.212	0.000	-1.96e+04	-8876.061
Q5_Software Engineer	-5539.5680	2292.779	-2.416	0.016	-1e+04	-1045.287
Q5_Statistician	-1.415e+04	4364.134	-3.243	0.001	-2.27e+04	-5596.983

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	2.648e+04	5515.996	4.801	0.000	1.57e+04	3.73e+04
Q2_Woman	-1.136e+04	1678.207	-6.768	0.000	-1.46e+04	-8068.294
Q4_Bachelor’s degree	9112.9305	5306.501	1.717	0.086	-1288.817	1.95e+04
Q4_Doctoral degree	4.116e+04	5457.030	7.543	0.000	3.05e+04	5.19e+04
Q4_Master’s degree	2.194e+04	5264.659	4.168	0.000	1.16e+04	3.23e+04
Q4_No formal education past high school	4480.3109	7992.155	0.561	0.575	-1.12e+04	2.01e+04
Q4_Professional degree	1.409e+04	5935.740	2.374	0.018	2453.310	2.57e+04
Q4_Some college/university study without earning a bachelor’s degree	1.356e+04	6094.636	2.226	0.026	1617.591	2.55e+04
Q5_Business Analyst	-7794.0458	2924.883	-2.665	0.008	-1.35e+04	-2060.721
Q5_DBA/Database Engineer	-9304.2854	6313.022	-1.474	0.141	-2.17e+04	3070.434
Q5_Data Analyst	-1.809e+04	2434.246	-7.430	0.000	-2.29e+04	-1.33e+04
Q5_Data Engineer	-4449.6035	3689.488	-1.206	0.228	-1.17e+04	2782.492
Q5_Data Scientist	-402.2331	2127.984	-0.189	0.850	-4573.485	3769.019
Q5_Machine Learning Engineer	-8624.4866	2674.788	-3.224	0.001	-1.39e+04	-3381.396
Q5_Product/Project Manager	1.434e+04	3060.436	4.686	0.000	8340.668	2.03e+04
Q5_Research Scientist	-1.288e+04	2715.954	-4.744	0.000	-1.82e+04	-7559.700
Q5_Software Engineer	-6526.1595	2377.008	-2.746	0.006	-1.12e+04	-1866.774
Q5_Statistician	-1.72e+04	4388.765	-3.920	0.000	-2.58e+04	-8601.639
Q7_Part_1_Python	-2089.8217	1672.978	-1.249	0.212	-5369.175	1189.532
Q7_Part_2_R	6332.3808	1487.724	4.256	0.000	3416.160	9248.602
Q7_Part_3_SQL	9799.2102	1340.259	7.311	0.000	7172.049	1.24e+04
Q7_Part_4_C	-5592.6104	2049.608	-2.729	0.006	-9610.231	-1574.990
Q7_Part_5_C++	-3761.5088	1908.684	-1.971	0.049	-7502.891	-20.126
Q7_Part_6_Java	-1154.8178	1814.272	-0.637	0.524	-4711.135	2401.500
Q7_Part_7_Javascript	-4954.9170	1784.160	-2.777	0.005	-8452.209	-1457.625
Q7_Part_8_Julia	1.924e+04	4870.333	3.950	0.000	9692.458	2.88e+04
Q7_Part_9_Swift	1.497e+04	5645.352	2.652	0.008	3903.772	2.6e+04
Q7_Part_10_Bash	2.451e+04	1897.962	12.913	0.000	2.08e+04	2.82e+04
Q7_Part_11_MATLAB	-9398.2617	2095.059	-4.486	0.000	-1.35e+04	-5291.548
Q7_Part_12_None	4859.3494	6388.702	0.761	0.447	-7663.717	1.74e+04

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	2.303e+04	4971.296	4.633	0.000	1.33e+04	3.28e+04
Q2_Woman	-1.346e+04	1423.798	-9.453	0.000	-1.63e+04	-1.07e+04
Q4_Bachelor’s degree	1877.8259	4491.395	0.418	0.676	-6926.165	1.07e+04
Q4_Doctoral degree	1.935e+04	4631.697	4.177	0.000	1.03e+04	2.84e+04
Q4_Master’s degree	8351.0922	4456.400	1.874	0.061	-384.300	1.71e+04
Q4_No formal education past high school	-2951.3930	6750.021	-0.437	0.662	-1.62e+04	1.03e+04
Q4_Professional degree	1.28e+04	5025.954	2.547	0.011	2950.481	2.27e+04
Q4_Some college/university study without earning a bachelor’s degree	-1827.7110	5152.296	-0.355	0.723	-1.19e+04	8271.771
Q5_Business Analyst	-1749.9121	2469.141	-0.709	0.479	-6589.898	3090.074
Q5_DBA/Database Engineer	-1641.0576	5322.343	-0.308	0.758	-1.21e+04	8791.749
Q5_Data Analyst	-1.166e+04	2057.328	-5.668	0.000	-1.57e+04	-7628.561
Q5_Data Engineer	-403.6505	3115.749	-0.130	0.897	-6511.112	5703.811
Q5_Data Scientist	4365.7392	1796.150	2.431	0.015	844.943	7886.535
Q5_Machine Learning Engineer	389.8339	2270.354	0.172	0.864	-4060.493	4840.161
Q5_Product/Project Manager	1.405e+04	2585.103	5.437	0.000	8986.708	1.91e+04
Q5_Research Scientist	-7161.9120	2297.496	-3.117	0.002	-1.17e+04	-2658.381
Q5_Software Engineer	-977.1483	2009.662	-0.486	0.627	-4916.469	2962.173
Q5_Statistician	-6398.9408	3711.459	-1.724	0.085	-1.37e+04	876.226
Q7_Part_1_Python	-186.4436	1418.933	-0.131	0.895	-2967.824	2594.936
Q7_Part_2_R	714.0109	1266.882	0.564	0.573	-1769.318	3197.340
Q7_Part_3_SQL	4678.6395	1138.072	4.111	0.000	2447.801	6909.478
Q7_Part_4_C	1908.0340	1747.381	1.092	0.275	-1517.165	5333.233
Q7_Part_5_C++	-1074.1109	1612.061	-0.666	0.505	-4234.058	2085.836
Q7_Part_6_Java	2649.4801	1538.619	1.722	0.085	-366.505	5665.466
Q7_Part_7_Javascript	-3155.3535	1512.138	-2.087	0.037	-6119.431	-191.276
Q7_Part_8_Julia	1.117e+04	4112.684	2.715	0.007	3105.609	1.92e+04
Q7_Part_9_Swift	6519.2254	4762.432	1.369	0.171	-2816.049	1.59e+04
Q7_Part_10_Bash	1.18e+04	1618.805	7.290	0.000	8627.637	1.5e+04
Q7_Part_11_MATLAB	-5487.3901	1778.945	-3.085	0.002	-8974.461	-2000.319
Q7_Part_12_None	-1.224e+04	5398.276	-2.267	0.023	-2.28e+04	-1654.161
Q3_Argentina	-1.377e+04	6123.882	-2.249	0.025	-2.58e+04	-1769.132
Q3_Australia	5.545e+04	4825.082	11.491	0.000	4.6e+04	6.49e+04
Q3_Bangladesh	-2.429e+04	7956.870	-3.052	0.002	-3.99e+04	-8691.286
Q3_Belarus	-1.617e+04	8824.264	-1.832	0.067	-3.35e+04	1127.606
Q3_Belgium	2.498e+04	9055.882	2.758	0.006	7225.690	4.27e+04
Q3_Brazil	-9097.0594	3120.326	-2.915	0.004	-1.52e+04	-2980.625
Q3_Canada	4.142e+04	4188.497	9.890	0.000	3.32e+04	4.96e+04
Q3_Chile	-7469.8282	6927.236	-1.078	0.281	-2.1e+04	6108.874
Q3_China	4801.5007	4539.215	1.058	0.290	-4096.227	1.37e+04
Q3_Colombia	-1.771e+04	5144.552	-3.442	0.001	-2.78e+04	-7623.734
Q3_Egypt	-1.774e+04	5811.043	-3.052	0.002	-2.91e+04	-6346.460
Q3_France	1.136e+04	4224.719	2.688	0.007	3075.901	1.96e+04
Q3_Germany	3.797e+04	3823.698	9.930	0.000	3.05e+04	4.55e+04
Q3_Ghana	3025.6666	1.16e+04	0.261	0.794	-1.97e+04	2.57e+04
Q3_Greece	-8652.4807	6765.173	-1.279	0.201	-2.19e+04	4608.548
Q3_India	-1.085e+04	2196.452	-4.942	0.000	-1.52e+04	-6549.450
Q3_Indonesia	-9925.2258	5120.181	-1.938	0.053	-2e+04	111.305
Q3_Iran, Islamic Republic of...	-2.356e+04	6281.990	-3.751	0.000	-3.59e+04	-1.12e+04
Q3_Ireland	2.449e+04	9185.606	2.667	0.008	6488.006	4.25e+04
Q3_Israel	7.696e+04	6912.318	11.134	0.000	6.34e+04	9.05e+04
Q3_Italy	5438.5370	4313.546	1.261	0.207	-3016.836	1.39e+04
Q3_Japan	1.511e+04	3292.577	4.589	0.000	8655.411	2.16e+04
Q3_Kenya	-1.918e+04	6486.710	-2.956	0.003	-3.19e+04	-6462.313
Q3_Malaysia	-4936.4603	7379.266	-0.669	0.504	-1.94e+04	9528.307
Q3_Mexico	-8056.6910	4863.886	-1.656	0.098	-1.76e+04	1477.453
Q3_Morocco	-2.376e+04	7058.710	-3.366	0.001	-3.76e+04	-9919.708
Q3_Nepal	-1.523e+04	1.19e+04	-1.284	0.199	-3.85e+04	8028.379
Q3_Netherlands	3.717e+04	5382.807	6.905	0.000	2.66e+04	4.77e+04
Q3_Nigeria	-1.805e+04	3866.361	-4.669	0.000	-2.56e+04	-1.05e+04
Q3_Pakistan	-1.83e+04	5132.118	-3.567	0.000	-2.84e+04	-8244.491
Q3_Peru	-1.501e+04	6919.618	-2.168	0.030	-2.86e+04	-1441.296
Q3_Philippines	-1.091e+04	7728.146	-1.411	0.158	-2.61e+04	4241.768
Q3_Poland	-1720.5819	5669.763	-0.303	0.762	-1.28e+04	9393.234
Q3_Portugal	-1203.7407	5961.484	-0.202	0.840	-1.29e+04	1.05e+04
Q3_Republic of Korea	3295.7740	8311.136	0.397	0.692	-1.3e+04	1.96e+04
Q3_Romania	-8390.3897	8713.273	-0.963	0.336	-2.55e+04	8689.285
Q3_Russia	-1.126e+04	3397.957	-3.314	0.001	-1.79e+04	-4600.425
Q3_Saudi Arabia	3493.1809	7872.797	0.444	0.657	-1.19e+04	1.89e+04
Q3_Singapore	2.755e+04	5928.575	4.647	0.000	1.59e+04	3.92e+04
Q3_South Africa	8659.0332	6189.325	1.399	0.162	-3473.224	2.08e+04
Q3_South Korea	8805.0625	5936.789	1.483	0.138	-2832.176	2.04e+04
Q3_Spain	1.065e+04	3917.096	2.720	0.007	2975.713	1.83e+04
Q3_Sri Lanka	-1.717e+04	8716.519	-1.970	0.049	-3.43e+04	-83.953
Q3_Sweden	2.569e+04	7371.012	3.485	0.000	1.12e+04	4.01e+04
Q3_Switzerland	8.518e+04	7804.316	10.914	0.000	6.99e+04	1e+05
Q3_Taiwan	-7931.8019	5122.522	-1.548	0.122	-1.8e+04	2109.317
Q3_Thailand	1716.8745	6470.016	0.265	0.791	-1.1e+04	1.44e+04
Q3_Tunisia	-1.022e+04	7672.124	-1.332	0.183	-2.53e+04	4816.895
Q3_Turkey	-1.581e+04	4528.834	-3.491	0.000	-2.47e+04	-6933.438
Q3_Ukraine	-1.218e+04	5297.113	-2.299	0.022	-2.26e+04	-1792.397
Q3_United Arab Emirates	3.101e+04	8031.475	3.861	0.000	1.53e+04	4.68e+04
Q3_United Kingdom of Great Britain and Northern Ireland	4.806e+04	3401.737	14.128	0.000	4.14e+04	5.47e+04
Q3_United States of America	8.678e+04	2331.742	37.217	0.000	8.22e+04	9.14e+04
Q3_Viet Nam	-1.713e+04	6065.842	-2.823	0.005	-2.9e+04	-5236.170

Attempting to quantify Gender differences using the Kaggle Developer Survey¶

Building a Model¶

Lasso regression, Random Forest¶

Dep. Variable:	aprox_salary	R-squared:	0.006
Model:	OLS	Adj. R-squared:	0.006
Method:	Least Squares	F-statistic:	59.46
Date:	Wed, 27 Jul 2022	Prob (F-statistic):	1.36e-14
Time:	15:54:53	Log-Likelihood:	-1.3189e+05
No. Observations:	10555	AIC:	2.638e+05
Df Residuals:	10553	BIC:	2.638e+05
Df Model:	1
Covariance Type:	nonrobust

Omnibus:	7193.154	Durbin-Watson:	1.978
Prob(Omnibus):	0.000	Jarque-Bera (JB):	111688.022
Skew:	3.122	Prob(JB):	0.00
Kurtosis:	17.662	Cond. No.	2.81

	Q2_Man	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer
2	1	0	0	1	1	0	0	0
3	1	1	0	0	0	0	0	1
4	1	0	0	1	0	1	0	0
9	1	0	1	0	0	1	0	0
12	1	0	1	0	0	0	1	0

Omnibus:	7260.254	Durbin-Watson:	1.980
Prob(Omnibus):	0.000	Jarque-Bera (JB):	119737.467
Skew:	3.138	Prob(JB):	0.00
Kurtosis:	18.260	Cond. No.	28.2

	Q2_Man	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer	Q7_Part_1_Python	Q7_Part_2_R	Q7_Part_3_SQL	Q7_Part_6_Java	Q7_Part_7_Javascript	Q7_Part_10_Bash
2	1	0	0	1	1	0	0	0	1	1	1	0	0	0
3	1	1	0	0	0	0	0	1	0	0	0	1	1	1
4	1	0	0	1	0	1	0	0	1	0	1	0	0	1
9	1	0	1	0	0	1	0	0	1	0	1	0	0	1
12	1	0	1	0	0	0	1	0	0	1	0	0	0	0

Omnibus:	7389.009	Durbin-Watson:	1.989
Prob(Omnibus):	0.000	Jarque-Bera (JB):	130840.255
Skew:	3.190	Prob(JB):	0.00
Kurtosis:	19.025	Cond. No.	37.8

	Q2_Man	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer	Q7_Part_1_Python	Q7_Part_2_R	Q7_Part_3_SQL	Q7_Part_6_Java	Q7_Part_7_Javascript	Q7_Part_10_Bash	Q3_Argentina	Q3_Germany	Q3_United States of America
2	1	0	0	1	1	0	0	0	1	1	1	0	0	0	0	0	1
3	1	1	0	0	0	0	0	1	0	0	0	1	1	1	1	0	0
4	1	0	0	1	0	1	0	0	1	0	1	0	0	1	0	0	1
9	1	0	1	0	0	1	0	0	1	0	1	0	0	1	0	1	0
12	1	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	1

Omnibus:	8936.415	Durbin-Watson:	1.969
Prob(Omnibus):	0.000	Jarque-Bera (JB):	362278.541
Skew:	3.879	Prob(JB):	0.00
Kurtosis:	30.632	Cond. No.	49.6

Omnibus:	9292.806	Durbin-Watson:	1.977
Prob(Omnibus):	0.000	Jarque-Bera (JB):	433887.014
Skew:	4.076	Prob(JB):	0.00
Kurtosis:	33.334	Cond. No.	52.9

	salary_min	salary_max	aprox_salary	Q1_22-24	Q1_25-29	Q1_35-39	Q1_50-54	Q1_55-59	Q2_Woman	Q3_Belarus	Q3_Canada	Q3_India	Q3_Pakistan	Q3_United States of America	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Analyst	Q5_Machine Learning Engineer	Q5_Other	Q5_Product/Project Manager	Q5_Statistician	Q6_1-2 years	Q6_3-5 years	Q6_5-10 years	Q6_I have never written code	Q7_Part_1_Python	Q7_Part_2_R	Q7_Part_3_SQL	Q7_Part_5_C++	Q7_Part_7_Javascript	Q7_OTHER_Other	Q8_MATLAB	Q8_Python	Q8_SQL	Q9_Part_1_Jupyter (JupyterLab, Jupyter Notebooks, etc)	Q9_Part_2_ RStudio	Q9_Part_3_Visual Studio	Q9_Part_4_Visual Studio Code (VSCode)	Q9_Part_6_ Spyder	Q9_Part_7_ Notepad++	Q9_Part_11_None	Q10_Part_1_ Kaggle Notebooks	Q10_Part_5_ Binder / JupyterHub	Q10_Part_7_ IBM Watson Studio	Q10_Part_8_ Amazon Sagemaker Studio	Q10_Part_9_ Amazon EMR Notebooks	Q10_OTHER_Other	Q11_A deep learning workstation (NVIDIA GTX, LambdaLabs, etc)	Q11_A personal computer or laptop	Q12_Part_1_GPUs	Q12_Part_2_TPUs	Q12_Part_3_None	Q12_OTHER_Other	Q13_Never	Q13_Once	Q14_Part_1_ Matplotlib	Q14_Part_2_ Seaborn	Q14_Part_3_ Plotly / Plotly Express	Q14_Part_4_ Ggplot / ggplot2	Q14_Part_5_ Shiny	Q14_Part_6_ D3 js	Q14_Part_8_ Bokeh	Q14_Part_9_ Geoplotlib	Q14_Part_11_None	Q14_OTHER_Other	Q15_1-2 years	Q15_4-5 years	Q15_Under 1 year	Q16_Part_1_ Scikit-learn	Q16_Part_2_ TensorFlow	Q16_Part_3_ Keras	Q16_Part_7_ Xgboost	Q16_Part_9_ CatBoost	Q16_Part_15_None	Q16_OTHER_Other	Q17_Part_1_Linear or Logistic Regression	Q17_Part_2_Decision Trees or Random Forests	Q17_Part_3_Gradient Boosting Machines (xgboost, lightgbm, etc)	Q17_Part_4_Bayesian Approaches	Q17_Part_5_Evolutionary Approaches	Q17_Part_6_Dense Neural Networks (MLPs, etc)	Q17_Part_7_Convolutional Neural Networks	Q17_Part_9_Recurrent Neural Networks	Q17_OTHER_Other	Q18_Part_2_Image segmentation methods (U-Net, Mask R-CNN, etc)	Q18_Part_4_Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	Q18_Part_5_Generative Networks (GAN, VAE, etc)	Q18_OTHER_Other	Q19_Part_1_Word embeddings/vectors (GLoVe, fastText, word2vec)	Q19_Part_4_Transformer language models (GPT-3, BERT, XLnet, etc)	Q19_OTHER_Other	Q20_0-49 employees	Q20_1000-9,999 employees	Q20_250-999 employees	Q20_50-249 employees	Q21_0	Q21_1-2	Q21_20+	Q21_3-4	Q21_5-9	Q22_I do not know	Q22_We are exploring ML methods (and may one day put a model into production)	Q22_We recently started using ML methods (i.e., models in production for less than 2 years)	Q22_We use ML methods for generating insights (but do not put working models into production)	Q23_Part_1_Analyze and understand data to influence product or business decisions	Q23_Part_2_Build and/or run the data infrastructure that my business uses for storing, analyzing, and operationalizing data	Q23_Part_3_Build prototypes to explore applying machine learning to new areas	Q23_Part_4_Build and/or run a machine learning service that operationally improves my product or workflows	Q23_Part_5_Experimentation and iteration to improve existing ML models	Q23_Part_6_Do research that advances the state of the art of machine learning	Q23_OTHER_Other	...	Q29_A_Part_11_Amazon Redshift	Q29_A_OTHER_Other	Q30_Amazon Redshift	Q30_PostgresSQL	Q31_A_Part_2_Microsoft Power BI	Q31_A_Part_5_Tableau	Q31_A_Part_8_Qlik	Q31_A_OTHER_Other	Q32_Microsoft Power BI	Q32_Tableau	Q33_A_Part_1_Automated data augmentation (e.g. imgaug, albumentations)	Q33_A_Part_3_Automated model selection (e.g. auto-sklearn, xcessiv)	Q33_A_Part_6_Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)	Q33_A_Part_7_No / None	Q33_A_OTHER_Other	Q34_A_Part_3_ Databricks AutoML	Q34_A_Part_4_ DataRobot AutoML	Q34_A_Part_6_ Auto-Keras	Q34_A_Part_7_ Auto-Sklearn	Q34_A_Part_10_ MLbox	Q34_A_OTHER_Other	Q35_A_Part_4_ Sacred + Omniboard	Q35_A_Part_5_ TensorBoard	Q35_A_Part_8_ Trains	Q35_A_Part_10_No / None	Q35_A_OTHER_Other	Q36_Part_4_ GitHub	Q36_Part_5_ Personal blog	Q36_Part_8_ Shiny	Q36_OTHER_Other	Q37_Part_1_Coursera	Q37_Part_3_Kaggle Learn Courses	Q37_Part_4_DataCamp	Q37_Part_6_Udacity	Q37_Part_7_Udemy	Q37_Part_8_LinkedIn Learning	Q37_Part_10_University Courses (resulting in a university degree)	Q38_Advanced statistical software (SPSS, SAS, etc.)	Q38_Local development environments (RStudio, JupyterLab, etc.)	Q39_Part_1_Twitter (data science influencers)	Q39_Part_2_Email newsletters (Data Elixir, O'Reilly Data & AI, etc)	Q39_Part_4_Kaggle (notebooks, forums, etc)	Q39_Part_5_Course Forums (forums.fast.ai, Coursera forums, etc)	Q39_Part_6_YouTube (Kaggle YouTube, Cloud AI Adventures, etc)	Q39_Part_7_Podcasts (Chai Time Data Science, O’Reilly Data Show, etc)	Q39_Part_8_Blogs (Towards Data Science, Analytics Vidhya, etc)	Q39_Part_10_Slack Communities (ods.ai, kagglenoobs, etc)	Q39_OTHER_Other	Q26_B_Part_3_ Google Cloud Platform (GCP)	Q26_B_Part_4_ IBM Cloud / Red Hat	Q26_B_Part_11_None	Q27_B_Part_5_ Microsoft Azure Container Instances	Q27_B_Part_7_ Google Cloud Compute Engine	Q27_B_Part_10_ Google Cloud App Engine	Q28_B_Part_1_ Amazon SageMaker	Q28_B_Part_6_ Google Cloud AI Platform / Google Cloud ML Engine	Q28_B_Part_7_ Google Cloud Video AI	Q29_B_Part_1_MySQL	Q29_B_Part_3_SQLite	Q29_B_Part_4_Oracle Database	Q29_B_Part_7_IBM Db2	Q29_B_Part_9_Microsoft Access	Q31_B_Part_1_Microsoft Power BI	Q31_B_Part_5_Tableau	Q33_B_Part_7_None	Q35_B_Part_2_ Weights & Biases	salary_cleaned_1000-1999	salary_cleaned_125000-149999	salary_cleaned_15000-19999
21	1000	1999	1499.5	1	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	1	0	1	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0	1	1	0	1	0	0	0	0	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0
23	125000	149999	137499.5	0	0	0	0	1	1	0	1	0	0	0	0	0	1	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	1	1	1	0	0	1	0	0	1	0	1	0	1	1	0	0	0	0	1	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	...	1	0	1	0	1	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	1	0	0	0	1	0	0	1	0	0	1	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
31	15000	19999	17499.5	0	0	1	0	0	1	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	1	0	1	1	1	0	1	1	0	0	1	1	1	1	1	1	1	0	0	1	0	0	0	1	1	0	1	1	0	1	0	1	1	0	1	1	1	1	0	0	0	1	0	1	0	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	0	0	0	0	1	0	0	0	0	1	1	1	1	1	1	1	1	...	0	1	0	1	1	1	1	1	1	0	1	1	1	0	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
35	125000	149999	137499.5	0	0	0	1	0	1	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
45	1000	1999	1499.5	0	1	0	0	0	1	1	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	1	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	0	0	1	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	1.998e+04	5227.841	3.821	0.000	9729.629	3.02e+04
Q2_Woman	-9850.1672	1391.441	-7.079	0.000	-1.26e+04	-7122.677
Q4_Bachelor’s degree	2358.8884	4362.365	0.541	0.589	-6192.180	1.09e+04
Q4_Doctoral degree	1.066e+04	4514.067	2.362	0.018	1813.450	1.95e+04
Q4_Master’s degree	6061.2281	4330.350	1.400	0.162	-2427.085	1.45e+04
Q4_No formal education past high school	-4032.2904	6552.793	-0.615	0.538	-1.69e+04	8812.434
Q4_Professional degree	9471.6892	4882.542	1.940	0.052	-99.024	1.9e+04
Q4_Some college/university study without earning a bachelor’s degree	379.7929	5003.560	0.076	0.939	-9428.139	1.02e+04
Q5_Business Analyst	-221.6800	2399.179	-0.092	0.926	-4924.530	4481.169
Q5_DBA/Database Engineer	-7440.0709	5178.243	-1.437	0.151	-1.76e+04	2710.273
Q5_Data Analyst	-8205.4418	2008.429	-4.086	0.000	-1.21e+04	-4268.538
Q5_Data Engineer	-3306.4966	3039.060	-1.088	0.277	-9263.635	2650.641
Q5_Data Scientist	4891.9299	1769.479	2.765	0.006	1423.414	8360.446
Q5_Machine Learning Engineer	2304.1423	2233.703	1.032	0.302	-2074.341	6682.626
Q5_Product/Project Manager	1.132e+04	2514.215	4.503	0.000	6392.083	1.62e+04
Q5_Research Scientist	-7513.2542	2247.037	-3.344	0.001	-1.19e+04	-3108.632
Q5_Software Engineer	-5280.5330	1995.056	-2.647	0.008	-9191.223	-1369.843
Q5_Statistician	-5492.2495	3612.777	-1.520	0.128	-1.26e+04	1589.482
Q7_Part_1_Python	311.0264	1574.945	0.197	0.843	-2776.167	3398.220
Q7_Part_2_R	-694.3749	1246.093	-0.557	0.577	-3136.954	1748.205
Q7_Part_3_SQL	1141.8799	1125.737	1.014	0.310	-1064.778	3348.538
Q7_Part_4_C	13.6995	1703.929	0.008	0.994	-3326.326	3353.725
Q7_Part_5_C++	-2100.2435	1569.196	-1.338	0.181	-5176.168	975.681
Q7_Part_6_Java	141.7489	1498.198	0.095	0.925	-2795.005	3078.503
Q7_Part_7_Javascript	-3366.5836	1479.874	-2.275	0.023	-6267.419	-465.748
Q7_Part_8_Julia	9157.2964	3995.353	2.292	0.022	1325.642	1.7e+04
Q7_Part_9_Swift	5386.1976	4628.395	1.164	0.245	-3686.340	1.45e+04
Q7_Part_10_Bash	7736.1222	1583.277	4.886	0.000	4632.596	1.08e+04
Q7_Part_11_MATLAB	-5133.0776	1729.888	-2.967	0.003	-8523.988	-1742.167
Q7_Part_12_None	-9453.0678	5348.503	-1.767	0.077	-1.99e+04	1031.019
Q3_Argentina	-1.859e+04	5948.935	-3.124	0.002	-3.02e+04	-6926.095
Q3_Australia	4.951e+04	4692.555	10.550	0.000	4.03e+04	5.87e+04
Q3_Bangladesh	-1.723e+04	7730.119	-2.228	0.026	-3.24e+04	-2073.911
Q3_Belarus	-1.584e+04	8565.637	-1.849	0.064	-3.26e+04	951.447
Q3_Belgium	1.693e+04	8797.272	1.924	0.054	-316.344	3.42e+04
Q3_Brazil	-1.339e+04	3036.307	-4.409	0.000	-1.93e+04	-7436.254
Q3_Canada	3.663e+04	4070.898	8.999	0.000	2.87e+04	4.46e+04
Q3_Chile	-8582.8955	6724.484	-1.276	0.202	-2.18e+04	4598.377
Q3_China	7905.5053	4412.585	1.792	0.073	-744.003	1.66e+04

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	8183.1110	1.07e+04	0.763	0.445	-1.28e+04	2.92e+04
Q4_Bachelor’s degree	3056.2863	9321.455	0.328	0.743	-1.52e+04	2.13e+04
Q4_Doctoral degree	1.365e+04	9532.967	1.432	0.152	-5046.439	3.24e+04
Q4_Master’s degree	9813.5973	9257.062	1.060	0.289	-8343.732	2.8e+04
Q4_No formal education past high school	4502.3068	2.37e+04	0.190	0.849	-4.2e+04	5.1e+04
Q4_Professional degree	1.096e+04	1.04e+04	1.051	0.294	-9504.093	3.14e+04
Q4_Some college/university study without earning a bachelor’s degree	-2733.4509	1.16e+04	-0.235	0.814	-2.56e+04	2.01e+04
Q5_Business Analyst	8338.4117	4835.815	1.724	0.085	-1146.832	1.78e+04
Q5_DBA/Database Engineer	-8461.8924	1.3e+04	-0.652	0.515	-3.39e+04	1.7e+04
Q5_Data Analyst	-505.3368	3921.993	-0.129	0.897	-8198.158	7187.485
Q5_Data Engineer	9645.7086	6837.554	1.411	0.159	-3765.860	2.31e+04
Q5_Data Scientist	9812.0362	3762.486	2.608	0.009	2432.082	1.72e+04
Q5_Machine Learning Engineer	989.9825	5247.103	0.189	0.850	-9301.985	1.13e+04
Q5_Product/Project Manager	2.074e+04	6223.540	3.333	0.001	8536.779	3.3e+04
Q5_Research Scientist	-2633.8866	4540.896	-0.580	0.562	-1.15e+04	6272.886
Q5_Software Engineer	3043.3358	4454.516	0.683	0.495	-5694.006	1.18e+04
Q5_Statistician	-721.8901	6659.766	-0.108	0.914	-1.38e+04	1.23e+04
Q7_Part_1_Python	-1987.9513	3034.027	-0.655	0.512	-7939.066	3963.163
Q7_Part_2_R	-3503.0673	2547.904	-1.375	0.169	-8500.673	1494.538
Q7_Part_3_SQL	-85.7876	2407.232	-0.036	0.972	-4807.470	4635.895
Q7_Part_4_C	3651.6790	3793.279	0.963	0.336	-3788.675	1.11e+04
Q7_Part_5_C++	441.8752	3593.246	0.123	0.902	-6606.124	7489.874
Q7_Part_6_Java	-521.6809	3375.162	-0.155	0.877	-7141.917	6098.555
Q7_Part_7_Javascript	-4303.9568	3475.900	-1.238	0.216	-1.11e+04	2513.873
Q7_Part_8_Julia	4.335e+04	1.55e+04	2.803	0.005	1.3e+04	7.37e+04
Q7_Part_9_Swift	1.23e+04	1.65e+04	0.746	0.456	-2e+04	4.46e+04
Q7_Part_10_Bash	8610.6046	4430.767	1.943	0.052	-80.155	1.73e+04
Q7_Part_11_MATLAB	-1.015e+04	3388.790	-2.995	0.003	-1.68e+04	-3501.060
Q7_Part_12_None	-2.098e+04	1.03e+04	-2.039	0.042	-4.12e+04	-797.154
Q3_Argentina	-1.308e+04	1.3e+04	-1.007	0.314	-3.86e+04	1.24e+04
Q3_Australia	5.741e+04	1.36e+04	4.220	0.000	3.07e+04	8.41e+04
Q3_Bangladesh	-2.112e+04	2.5e+04	-0.846	0.398	-7.01e+04	2.79e+04
Q3_Belarus	-8172.4748	1.96e+04	-0.417	0.677	-4.66e+04	3.03e+04
Q3_Belgium	-1333.6895	1.96e+04	-0.068	0.946	-3.97e+04	3.7e+04
Q3_Brazil	-1.87e+04	7479.288	-2.500	0.013	-3.34e+04	-4027.631
Q3_Canada	3.986e+04	7989.572	4.989	0.000	2.42e+04	5.55e+04
Q3_Chile	-1.113e+04	1.67e+04	-0.665	0.506	-4.4e+04	2.17e+04
Q3_China	4.314e+04	1.28e+04	3.379	0.001	1.81e+04	6.82e+04
Q3_Colombia	-2.244e+04	1.36e+04	-1.655	0.098	-4.9e+04	4155.515

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	2.113e+04	5925.627	3.565	0.000	9509.814	3.27e+04
Q4_Bachelor’s degree	2269.9925	4888.071	0.464	0.642	-7311.771	1.19e+04
Q4_Doctoral degree	1.03e+04	5068.551	2.032	0.042	365.453	2.02e+04
Q4_Master’s degree	5361.5291	4853.175	1.105	0.269	-4151.832	1.49e+04
Q4_No formal education past high school	-4760.1632	7067.155	-0.674	0.501	-1.86e+04	9093.116
Q4_Professional degree	8982.4124	5472.166	1.641	0.101	-1744.315	1.97e+04
Q4_Some college/university study without earning a bachelor’s degree	43.3111	5553.019	0.008	0.994	-1.08e+04	1.09e+04
Q5_Business Analyst	-2030.1665	2720.312	-0.746	0.456	-7362.616	3302.283
Q5_DBA/Database Engineer	-8956.3773	5665.434	-1.581	0.114	-2.01e+04	2149.200
Q5_Data Analyst	-1.004e+04	2301.624	-4.364	0.000	-1.46e+04	-5532.784
Q5_Data Engineer	-5313.9576	3377.355	-1.573	0.116	-1.19e+04	1306.450
Q5_Data Scientist	4194.0621	1985.915	2.112	0.035	301.204	8086.920
Q5_Machine Learning Engineer	1835.2211	2471.241	0.743	0.458	-3008.990	6679.432
Q5_Product/Project Manager	9501.9436	2762.356	3.440	0.001	4087.079	1.49e+04
Q5_Research Scientist	-8548.2046	2554.309	-3.347	0.001	-1.36e+04	-3541.161
Q5_Software Engineer	-6785.4557	2225.012	-3.050	0.002	-1.11e+04	-2423.911
Q5_Statistician	-6386.1253	4197.408	-1.521	0.128	-1.46e+04	1841.778
Q7_Part_1_Python	1221.7586	1805.164	0.677	0.499	-2316.786	4760.303
Q7_Part_2_R	0.2495	1408.716	0.000	1.000	-2761.163	2761.662
Q7_Part_3_SQL	1311.2347	1261.566	1.039	0.299	-1161.730	3784.200
Q7_Part_4_C	-495.6441	1897.756	-0.261	0.794	-4215.691	3224.403
Q7_Part_5_C++	-2469.2897	1741.525	-1.418	0.156	-5883.087	944.507
Q7_Part_6_Java	215.4759	1663.898	0.130	0.897	-3046.154	3477.106
Q7_Part_7_Javascript	-3106.4598	1634.430	-1.901	0.057	-6310.325	97.405
Q7_Part_8_Julia	7160.4571	4212.060	1.700	0.089	-1096.166	1.54e+04
Q7_Part_9_Swift	4699.3419	4898.201	0.959	0.337	-4902.280	1.43e+04
Q7_Part_10_Bash	7388.5771	1710.986	4.318	0.000	4034.644	1.07e+04
Q7_Part_11_MATLAB	-4377.7019	1976.712	-2.215	0.027	-8252.521	-502.883
Q7_Part_12_None	-6980.8375	6136.348	-1.138	0.255	-1.9e+04	5047.841
Q3_Argentina	-1.951e+04	6620.102	-2.947	0.003	-3.25e+04	-6533.388
Q3_Australia	4.908e+04	5045.418	9.728	0.000	3.92e+04	5.9e+04
Q3_Bangladesh	-1.612e+04	8235.724	-1.957	0.050	-3.23e+04	22.792
Q3_Belarus	-1.716e+04	9465.935	-1.813	0.070	-3.57e+04	1393.318
Q3_Belgium	2.052e+04	9765.840	2.101	0.036	1371.949	3.97e+04
Q3_Brazil	-1.275e+04	3322.699	-3.837	0.000	-1.93e+04	-6236.328
Q3_Canada	3.551e+04	4651.266	7.635	0.000	2.64e+04	4.46e+04
Q3_Chile	-7461.8209	7342.751	-1.016	0.310	-2.19e+04	6931.691
Q3_China	5485.7584	4756.146	1.153	0.249	-3837.403	1.48e+04
Q3_Colombia	-1.808e+04	5405.063	-3.345	0.001	-2.87e+04	-7483.048

	Q2_Man	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer
2	1	0	0	1	1	0	0	0
3	1	1	0	0	0	0	0	1
4	1	0	0	1	0	1	0	0
9	1	0	1	0	0	1	0	0
12	1	0	1	0	0	0	1	0

	Q2_Man	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer	Q7_Part_1_Python	Q7_Part_2_R	Q7_Part_3_SQL	Q7_Part_6_Java	Q7_Part_7_Javascript	Q7_Part_10_Bash
2	1	0	0	1	1	0	0	0	1	1	1	0	0	0
3	1	1	0	0	0	0	0	1	0	0	0	1	1	1
4	1	0	0	1	0	1	0	0	1	0	1	0	0	1
9	1	0	1	0	0	1	0	0	1	0	1	0	0	1
12	1	0	1	0	0	0	1	0	0	1	0	0	0	0

	Q2_Man	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer	Q7_Part_1_Python	Q7_Part_2_R	Q7_Part_3_SQL	Q7_Part_6_Java	Q7_Part_7_Javascript	Q7_Part_10_Bash	Q3_Argentina	Q3_Germany	Q3_United States of America
2	1	0	0	1	1	0	0	0	1	1	1	0	0	0	0	0	1
3	1	1	0	0	0	0	0	1	0	0	0	1	1	1	1	0	0
4	1	0	0	1	0	1	0	0	1	0	1	0	0	1	0	0	1
9	1	0	1	0	0	1	0	0	1	0	1	0	0	1	0	1	0
12	1	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	1

Omnibus:	1732.338	Durbin-Watson:	1.993
Prob(Omnibus):	0.000	Jarque-Bera (JB):	155341.130
Skew:	4.813	Prob(JB):	0.00
Kurtosis:	49.071	Cond. No.	72.6

Omnibus:	7619.234	Durbin-Watson:	2.002
Prob(Omnibus):	0.000	Jarque-Bera (JB):	320551.693
Skew:	3.950	Prob(JB):	0.00
Kurtosis:	31.368	Cond. No.	52.5

	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer	Q7_Part_1_Python	Q7_Part_2_R	Q7_Part_3_SQL	Q7_Part_6_Java	Q7_Part_7_Javascript	Q7_Part_10_Bash	Q3_Argentina	Q3_Germany	Q3_United States of America	Q6_1-2 years	Q6_10-20 years	Q6_5-10 years	Q20_0-49 employees	Q20_10,000 or more employees	Q20_1000-9,999 employees	Q20_250-999 employees	male_preds	female_preds	aprox_salary
2	0	0	1	1	0	0	0	1	1	1	0	0	0	0	0	1	0	0	1	0	1	0	0	126872.821615	114828.899434	112499.5
3	1	0	0	0	0	0	1	0	0	0	1	1	1	1	0	0	0	1	0	0	0	1	0	29968.773101	34281.097398	17499.5
4	0	0	1	0	1	0	0	1	0	1	0	0	1	0	0	1	0	0	1	0	0	0	1	132427.010478	122032.800965	137499.5
9	0	1	0	0	1	0	0	1	0	1	0	0	1	0	1	0	0	0	1	0	0	1	0	94000.507165	84820.444771	74999.5
12	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	1	1	0	0	1	0	0	0	91886.090551	76772.188267	34999.5

	Q2_Man	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer
2	1	0	0	1	1	0	0	0
3	1	1	0	0	0	0	0	1
4	1	0	0	1	0	1	0	0
9	1	0	1	0	0	1	0	0
12	1	0	1	0	0	0	1	0

	Q2_Man	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer	Q7_Part_1_Python	Q7_Part_2_R	Q7_Part_3_SQL	Q7_Part_6_Java	Q7_Part_7_Javascript	Q7_Part_10_Bash
2	1	0	0	1	1	0	0	0	1	1	1	0	0	0
3	1	1	0	0	0	0	0	1	0	0	0	1	1	1
4	1	0	0	1	0	1	0	0	1	0	1	0	0	1
9	1	0	1	0	0	1	0	0	1	0	1	0	0	1
12	1	0	1	0	0	0	1	0	0	1	0	0	0	0

	Q2_Man	Q4_Bachelor’s degree	Q4_Doctoral degree	Q4_Master’s degree	Q5_Data Engineer	Q5_Data Scientist	Q5_Research Scientist	Q5_Software Engineer	Q7_Part_1_Python	Q7_Part_2_R	Q7_Part_3_SQL	Q7_Part_6_Java	Q7_Part_7_Javascript	Q7_Part_10_Bash	Q3_Argentina	Q3_Germany	Q3_United States of America
2	1	0	0	1	1	0	0	0	1	1	1	0	0	0	0	0	1
3	1	1	0	0	0	0	0	1	0	0	0	1	1	1	1	0	0
4	1	0	0	1	0	1	0	0	1	0	1	0	0	1	0	0	1
9	1	0	1	0	0	1	0	0	1	0	1	0	0	1	0	1	0
12	1	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	1